1. A system for enhancing and reconstructing environmental images using deep learning, comprising: an image input interface configured to receive an environmental image; a preprocessing engine configured to normalize and segment said environmental image into relevant components; a deep learning-based enhancement module trained on a plurality of environmental image datasets, configured to apply a series of transformations to said segmented components to enhance clarity, contrast, and resolution; a reconstruction unit incorporating generative network architectures to rebuild the segmented components into a singular enhanced image; and an output module configured to display and store the reconstructed enhanced image.

2. The system of claim 1, wherein the deep learning-based enhancement module comprises multiple convolutional neural networks (CNN) layers, each layer being specialized to target specific environmental artifacts such as haze, glare, or shadows.

3. The system of claim 1, further comprising: a feedback loop wherein the reconstructed enhanced image is cross-referenced with a ground-truth image, and discrepancies are utilized to retrain the deep learning-based enhancement module.

4. The system of claim 1, wherein the preprocessing engine is further configured to detect regions of high importance using saliency maps, thereby directing more computational resources towards enhancing those regions.

5. The system of claim 1, wherein the reconstruction unit further comprises a recurrent neural network (RNN) component, configured to sequence the reconstructed components in a temporal manner to account for dynamic environmental changes over time.

6. A method for enhancing and reconstructing environmental images using deep learning, comprising the steps of: receiving an environmental image; normalizing and segmenting the image into relevant components; processing the segmented components through a trained deep learning model to enhance clarity, contrast, and resolution; employing a generative network to reconstruct the enhanced components into a singular image; and outputting the reconstructed enhanced image.

7. The method of claim 6, further comprising: applying multiple convolutional neural network (CNN) layers during the enhancement step, with each layer targeting specific environmental artifacts such as haze, glare, or shadows.

8. The method of claim 6, further comprising: cross-referencing the reconstructed enhanced image with a ground-truth image; and utilizing discrepancies detected during the cross-referencing step to iteratively retrain the deep learning model.

9. The method of claim 6, further comprising: during the segmentation step, employing saliency maps to detect regions of high importance in the environmental image; and directing more computational resources towards enhancing the detected regions of high importance.

10. The method of claim 6, further comprising: using a recurrent neural network (RNN) during the reconstruction step, sequencing the enhanced components in a temporal manner to account for dynamic environmental changes over time. DESIGN OF A DEEP LEARNING-BASED MODEL TO ENHANCE AND RECONSTRUCT THE ENVIRONMENTAL IMAGE Abstract The disclosure introduces a sophisticated system for the enhancement and reconstruction of environmental images through deep learning methodologies. The system consists of an image input interface adept at collecting environmental images, paired with a preprocessing engine designed to normalize and divide the image into pivotal components. Building on the foundation, a uniquely trained enhancement module, ingrained with deep learning capabilities and grounded in extensive environmental image datasets, performs transformative operations on the segmented components, intensifying their clarity, contrast, and resolution. In the subsequent phase, a reconstruction unit, fortified with generative network frameworks, meticulously assembles said enhanced segments, culminating in a singular, augmented image. Completing the system, an output module is provisioned to both exhibit and archive the resultant improved image, marking a trailblazing advancement in environmental image processing. , Claims:Claims :

1. A system for enhancing and reconstructing environmental images using deep learning, comprising: an image input interface configured to receive an environmental image; a preprocessing engine configured to normalize and segment said environmental image into relevant components; a deep learning-based enhancement module trained on a plurality of environmental image datasets, configured to apply a series of transformations to said segmented components to enhance clarity, contrast, and resolution; a reconstruction unit incorporating generative network architectures to rebuild the segmented components into a singular enhanced image; and an output module configured to display and store the reconstructed enhanced image.

2. The system of claim 1, wherein the deep learning-based enhancement module comprises multiple convolutional neural networks (CNN) layers, each layer being specialized to target specific environmental artifacts such as haze, glare, or shadows.

3. The system of claim 1, further comprising: a feedback loop wherein the reconstructed enhanced image is cross-referenced with a ground-truth image, and discrepancies are utilized to retrain the deep learning-based enhancement module.

4. The system of claim 1, wherein the preprocessing engine is further configured to detect regions of high importance using saliency maps, thereby directing more computational resources towards enhancing those regions.

5. The system of claim 1, wherein the reconstruction unit further comprises a recurrent neural network (RNN) component, configured to sequence the reconstructed components in a temporal manner to account for dynamic environmental changes over time.

6. A method for enhancing and reconstructing environmental images using deep learning, comprising the steps of: receiving an environmental image; normalizing and segmenting the image into relevant components; processing the segmented components through a trained deep learning model to enhance clarity, contrast, and resolution; employing a generative network to reconstruct the enhanced components into a singular image; and outputting the reconstructed enhanced image.

7. The method of claim 6, further comprising: applying multiple convolutional neural network (CNN) layers during the enhancement step, with each layer targeting specific environmental artifacts such as haze, glare, or shadows.

8. The method of claim 6, further comprising: cross-referencing the reconstructed enhanced image with a ground-truth image; and utilizing discrepancies detected during the cross-referencing step to iteratively retrain the deep learning model.

9. The method of claim 6, further comprising: during the segmentation step, employing saliency maps to detect regions of high importance in the environmental image; and directing more computational resources towards enhancing the detected regions of high importance.

10. The method of claim 6, further comprising: using a recurrent neural network (RNN) during the reconstruction step, sequencing the enhanced components in a temporal manner to account for dynamic environmental changes over time.

Description:DESIGN OF A DEEP LEARNING-BASED MODEL TO ENHANCE AND RECONSTRUCT THE ENVIRONMENTAL IMAGE
Field of the Invention
[0001] The present disclosure relates to a deep learning-based system and method for the enhancement and reconstruction of environmental images. The system accepts environmental images as input, then normalizes and segments them into pertinent components. Said segmented components are processed through a specialized deep learning model that applies multiple layers of convolutional neural networks to enhance image clarity, contrast, and resolution by targeting specific environmental artifacts such as haze, glare, and shadows. The enhanced components are subsequently reconstructed into a cohesive image utilizing generative network architectures, resulting in a superior quality output that is more representative of the real-world environment. The design offers significant advancements in the field of environmental image processing, ensuring clearer, artifact-free images that better reflect actual environmental conditions.
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Environmental imaging, a critical component of remote sensing, surveillance, and natural studies, has historically faced challenges due to varying external factors like atmospheric scattering, changing light conditions, obstructions, and the inherent noise in capturing devices. Traditional methods of image enhancement, rooted in techniques like histogram equalization, adaptive filtering, and wavelet transformations, have provided moderate improvements in image clarity but often fall short in handling complex environmental artifacts.
[0004] One of the earliest breakthroughs in the realm of image processing was the use of adaptive histogram equalization (AHE), which improved upon basic histogram equalization by considering small contextual regions in the image. The AHE provided better contrast and enhanced details, especially in regions obscured by shadows or glare. However, the technique sometimes resulted in amplified noise, making the images less visually appealing and harder to analyse.
[0005] Subsequent advancements led to the inception of wavelet-based techniques. Said techniques decomposed images into multi-resolution scales, allowing for targeted noise reduction and feature enhancement. While the wavelet approach excelled in extracting fine details, said approach sometimes lacked in preserving the overall continuity and naturalness of images.
[0006] To overcome the limitations of traditional methods, researchers started to explore model-based techniques, most notably those involving sparse representations. Sparse coding algorithms, like Orthogonal Matching Pursuit (OMP) and K-SVD, represented images as a linear combination of few atoms from a learned dictionary. Said methods, although promising, were computationally intensive and struggled to generalize across diverse environmental conditions.
[0007] With the resurgence of neural networks in the past decade, the field of image enhancement and reconstruction began to shift the focus towards deep learning. Pioneering works like AlexNet showcased the potential of Convolutional Neural Networks (CNNs) in image classification tasks. Soon after, researchers began adapting CNN architectures for image enhancement tasks. The strength of CNNs lies in their ability to hierarchically learn features from data, making them especially potent for tasks that involve spatial hierarchies, such as image reconstruction.
[0008] One notable prior art in the domain is the Deep Image Prior (DIP), demonstrated that the structure of CNNs alone, without any task-specific training, could be used for several image processing tasks, including denoising and super-resolution. DIP was a paradigm shift, suggesting that not only did learnt weights matter, but the architecture of the network per se encapsulated a significant prior about the image data.
[0009] Another breakthrough came with the introduction of Generative Adversarial Networks (GANs). GANs comprise two neural networks, namely a generator and a discriminator, that compete in a game-theoretic framework. In the context of environmental image enhancement, GANs can be trained to produce high-resolution, clear images from degraded inputs, while the discriminator ensures the outputs are indistinguishable from real, high-quality environmental photos. CycleGAN, a variant, was used to translate images from one domain (e.g., foggy environment) to another (e.g., clear day) without paired training data.
[00010] While deep learning-based models have shown remarkable results in environmental image enhancement and reconstruction, there remain challenges tied to computational costs, real-time processing, and handling of extreme environmental conditions. The design of newer architectures, which seamlessly blend the strengths of traditional methods with deep learning insights, holds the key to breakthroughs in the domain.
[00011] All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.
Summary
[00012] Various objects, features, and advantages of the disclosed subject matter can be more fully appreciated with reference to the following detailed description of the disclosed subject matter when considered in connection with the following drawings, in which like reference numerals identify like elements.
[00013] The present disclosure relates to a deep learning-based system and method for the enhancement and reconstruction of environmental images. The system accepts environmental images as input, then normalizes and segments them into pertinent components. Said segmented components are processed through a specialized deep learning model that applies multiple layers of convolutional neural networks to enhance image clarity, contrast, and resolution by targeting specific environmental artifacts such as haze, glare, and shadows. The enhanced components are subsequently reconstructed into a cohesive image utilizing generative network architectures, resulting in a superior quality output that is more representative of the real-world environment. The design offers significant advancements in the field of environmental image processing, ensuring clearer, artifact-free images that better reflect actual environmental conditions.
[00014] Environmental imaging, vital for numerous applications ranging from remote sensing to surveillance, has historically been plagued with challenges such as atmospheric interferences, unwanted glares, and rapid environmental shifts. Addressing said challenges, the proposed system harnesses the power of deep learning to significantly enhance and reconstruct such images.
[00015] At the system's onset, an image input interface is proficiently designed to capture environmental images, ensuring a robust foundation for the subsequent processing stages. Following the initial capture, a preprocessing engine delves into the intricate task of both normalizing said images and segmenting them into essential components. Such segmentation aids in breaking down complex images into more manageable and processable units.
[00016] The heart of the system, the deep learning-based enhancement module, is meticulously trained on an expansive array of environmental image datasets. By leveraging the training, the module applies a cascade of transformations to the segmented components. Said transformations are crafted to amplify the image's clarity, contrast, and resolution, drastically improving the overall quality. Notably, the enhancement module incorporates multiple layers of convolutional neural networks (CNN). Each of said CNN layers is tailored to address specific environmental artifacts, such as the pervasive haze, intrusive glares, or deep shadows, ensuring a comprehensive enhancement approach.
[00017] Once the enhancement phase concludes, the reconstruction unit steps in. Infused with generative network architectures, the unit is tasked with the pivotal role of weaving the enhanced segments back together, culminating in a singular, vastly improved image.
[00018] For output, a dedicated module is integrated into the system, designed to both vividly display the reconstructed image and securely archive the reconstructed image for reference or use.
[00019] Adding an extra layer of precision, the system integrates a feedback loop. Here, the freshly reconstructed image is juxtaposed against a ground-truth image. Any detected discrepancies between the two are harnessed to fine-tune and retrain the enhancement module, ensuring continuous improvement in the system's performance.
[00020] Furthermore, the preprocessing engine is equipped with a capability to detect regions deemed of high importance via saliency maps. The addition ensures that computational resources are strategically directed towards enhancing said critical regions, maximizing the image's overall quality.
[00021] Lastly, considering the dynamic nature of environments, the reconstruction unit is fortified with a recurrent neural network (RNN) component. The RNN component sequences the reconstructed segments in a time-sensitive manner, ensuring that the final image is not only of high quality but also accurately represents the evolving environmental conditions.
[00022] The enhancement and reconstruction of environmental images, particularly in the context of challenges posed by various external factors like atmospheric disturbances, light variations, and inherent imperfections in imaging devices, have ushered in the need for more sophisticated methodologies. A deep learning-based method has been devised, promising substantial improvements in environmental image processing.
[00023] The process begins by receiving an environmental image, ensuring a quality foundation for subsequent steps. Once the image is secured, said image undergoes normalization to ensure consistency in pixel values and is then segmented into distinct, relevant components. The segmentation eases the task by breaking down a potentially intricate image into more digestible units, ready for advanced processing.
[00024] The segmented components are then ushered into a rigorously trained deep learning model. The objective here is to amplify the core attributes of the image: the clarity, contrast, and resolution. The enhancement phase is bolstered by the implementation of multiple convolutional neural network (CNN) layers. Each of said layers is tailored to combat and rectify specific environmental artifacts, such as the obstructive haze, blinding glares, or stark shadows. By addressing said challenges on a layer-by-layer basis, the method ensures a nuanced and comprehensive enhancement of the image components.
[00025] Following the enhancement, a generative network, renowned for the capability to fabricate and reconstruct intricate patterns, takes charge. Said generative network’s role is to meticulously knit together the enhanced segments, resulting in a cohesive, singular image that stands as a testament to the combined steps of the process.
[00026] To ensure the image's quality and authenticity, an additional step involves cross-referencing the newly reconstructed image with a benchmark or ground-truth image. The comparative analysis brings to light any discrepancies, which then become instrumental in iteratively retraining the deep learning model. Such feedback-driven refinements promise continuous improvement in the system's performance over time.
[00027] Recognizing the need to prioritize certain segments of an image, during the segmentation phase, saliency maps are employed. Said maps highlight regions of high importance, ensuring that said critical areas receive more computational attention and resources during enhancement.
[00028] Lastly, accounting for the ever-changing dynamics of environmental conditions, the method incorporates a recurrent neural network (RNN) during the reconstruction phase. The inclusion ensures that the enhanced components are sequenced in a time-sensitive manner, resulting in an output that not only embodies high quality but also resonates with the real-time environmental nuances. Thus, the method presents a holistic and nuanced approach to environmental image enhancement and reconstruction, ensuring images that are both high in quality and true to their environmental context.
Brief Description of the Drawings
[00029] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00030] FIG. 1 showcases a skeletal overview of a system for enhancing and reconstructing environmental images using deep learning, according to some embodiments of the present disclosure.
[00031] FIG. 2 portrays a detailed schematic flow chart of a method for enhancing and reconstructing environmental images using deep learning, according to some embodiments of the present disclosure.
[00032] Fig. 3 represents a flowchart of method for enhancing and reconstructing environmental images, according to some embodiments of the present disclosure.
Detailed Description
[00033] The following is a detailed description of exemplary embodiments to illustrate the principles of the invention. The embodiments are provided to illustrate aspects of the invention, but the invention is not limited to any embodiment. The scope of the invention encompasses numerous alternatives, modifications and equivalent; it is limited only by the claims.
[00034] In view of the many possible embodiments to which the principles of the present discussion may be applied, it should be recognized that the embodiments described herein with respect to the drawing figures are meant to be illustrative only and should not be taken as limiting the scope of the claims. Therefore, the techniques as described herein contemplate all such embodiments as may come within the scope of the following claims and equivalents thereof.
[00035] The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different instances in the description and the figures may indicate similar or identical items.
[00036] Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
[00037] The present disclosure relates to a deep learning-based system and method for the enhancement and reconstruction of environmental images. The system accepts environmental images as input, then normalizes and segments them into pertinent components. Said segmented components are processed through a specialized deep learning model that applies multiple layers of convolutional neural networks to enhance image clarity, contrast, and resolution by targeting specific environmental artifacts such as haze, glare, and shadows. The enhanced components are subsequently reconstructed into a cohesive image utilizing generative network architectures, resulting in a superior quality output that is more representative of the real-world environment. The design offers significant advancements in the field of environmental image processing, ensuring clearer, artifact-free images that better reflect actual environmental conditions.
[00038] The use of deep learning in image processing has unlocked a plethora of opportunities to enhance and reconstruct environmental images, especially in the era of rapid technological advancement. Whether improving the clarity of aerial photographs, restoring

underwater images, or enhancing surveillance footage, the system 100 represents an approach of enhancing and reconstructing environmental images. The system comprises several key components that work in tandem to transform raw environmental images into clear, high-resolution representations. The comprehensive exploration delves into the system's architecture, functionalities, and the potential impact on various domains.
[00039] According to figurative elucidation of FIG. 1, illustrating an architectural setup of the system 100 that can comprise functional elements, not limited to an image input interface 102, a preprocessing engine 104, a deep learning-based enhancement module 106, a reconstruction unit 108, and an output module 110. A person ordinarily skilled in art would prefer those elements or components of the system 100, to be functionally or operationally coupled with each other, in accordance with the embodiments of present disclosure.
[00040] In yet another embodiment, the system 100 begins with an image input interface that serves as the point of entry for environmental images. Said images can be captured by a wide range of devices, including cameras, satellites, drones, and underwater sensors. The primary purpose of the interface is to receive and preprocess the environmental image data, preparing said data for the subsequent stages of enhancement and reconstruction. For instance, an aerial drone capturing images of a dense forest. Said raw images might suffer from various issues, such as atmospheric haze, low contrast, and pixelation. The image input interface takes said images and passes them through to the next stage, the preprocessing engine.
[00041] In yet another embodiment, the preprocessing engine is a vital component of the system, responsible for normalizing and segmenting the incoming environmental image into relevant components. The normalization process involves correcting for various environmental artifacts and imperfections in the raw image data. Said environmental artifacts and imperfections can include tasks like removing lens distortion, color correction, and white balance adjustments. For example, if the environmental image contains a distorted horizon due to the camera lens, the preprocessing engine rectifies the distortion, ensuring accurate representation.
[00042] Segmentation is the process of identifying and isolating different components within the environmental image. Said components could be objects of interest, such as buildings, vehicles, or natural features. Segmenting the image into relevant components helps the subsequent deep learning-based enhancement module focus on specific areas for improvement.
[00043] In an embodiment, continuing with the forest drone imagery example, the preprocessing engine segments the image into distinct components, such as trees, terrain, and sky, making easier for the system to target enhancements. The core of the system's image enhancement capability lies in the deep learning-based enhancement module. The module has been trained on a diverse range of environmental image datasets, allowing said module to apply a series of transformations to the segmented components. Said transformations are designed to enhance clarity, contrast, and resolution, resulting in a significantly improved image.
[00044] Within the enhancement module, multiple convolutional neural network (CNN) layers are employed. Each layer is specialized to target specific environmental artifacts that commonly affect images. For instance, one CNN layer may be dedicated to mitigating haze, while another focuses on reducing glare or shadows. By having specialized layers, the system can effectively address a wide array of image quality issues simultaneously. Returning to the forest drone imagery, the enhancement module would work to reduce the atmospheric haze that often affects aerial photos, making the image crisper and more detailed.
[00045] One of the system's unique features is the inclusion of a feedback loop. After the reconstructed enhanced image is generated, said image is cross-referenced with a ground-truth image, which serves as the ideal representation of the scene. Discrepancies between the reconstructed image and the ground truth are utilized to retrain the deep learning-based enhancement module. Consider a scenario where the system is used to enhance images of coral reefs taken underwater. By comparing the enhanced image with a ground-truth image captured by a specialized camera, the system can learn and adapt to better handle the challenges presented by underwater conditions, such as light scattering and color distortion.
[00046] In yet another embodiment, the preprocessing engine is further configured to detect regions of high importance within the segmented components using saliency maps. Said maps highlight areas of the image that are most critical for achieving a visually pleasing and informative representation. The system then allocates more computational resources towards enhancing said important regions. For example, in satellite imagery of disaster-stricken areas, the preprocessing engine might identify regions with signs of damage as high-importance areas. The preprocessing engine ensures that the system prioritizes the enhancement of said critical areas to aid in disaster assessment and response.
[00047] In yet another embodiment, the reconstruction unit is tasked with piecing together the segmented components that have undergone enhancement to create a singular, enhanced image. The reconstruction unit leverages generative network architectures, often associated with deep generative models like Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs). In scenarios involving surveillance cameras capturing images of a crowded street, the reconstruction unit would take the enhanced segments of the image (e.g., vehicles, pedestrians) and integrate them into a cohesive and clear representation of the entire scene.
[00048] In an embodiment, in situations where environmental conditions change over time, such as in monitoring weather patterns or traffic flow, the reconstruction unit incorporates a recurrent neural network (RNN) component. The RNN sequences the reconstructed components in a temporal manner, accounting for dynamic environmental changes over time. For instance, in a time-lapse sequence of a busy city street, the RNN ensures that the reconstructed image evolves smoothly, capturing the dynamic nature of the environment as vehicles and pedestrians move.
[00049] In yet another embodiment, the final output module of the system serves two primary functions, displaying the reconstructed enhanced image and storing enhanced image for reference or analysis. The displayed image can be presented to users in various formats, such as on a computer monitor, a mobile device, or even in augmented reality (AR) glasses for real-time situational awareness.
[00050] Additionally, the system 100 stores the reconstructed enhanced image for archiving, documentation, or further analysis. In scientific research, for instance, said enhanced images can be used to study changes in ecosystems over time or assess the impact of environmental factors on infrastructure.
[00051] Referring to one or more preceding embodiments, the system 100 for enhancing and reconstructing environmental images using deep learning represents a remarkable fusion of advanced technology and image processing capabilities. The system is adaptable to a wide range of applications, from improving the quality of satellite images for environmental monitoring to enhancing underwater photographs for marine research.
[00052] By leveraging deep learning, specialized CNN layers, feedback loops, importance-driven enhancement, and recurrent neural networks, the system addresses the unique challenges presented by environmental images. The system holds the potential to revolutionize how we perceive, analyze, and interact with visual data in fields as diverse as environmental science, surveillance, and remote sensing.
[00053] As technology continues to evolve, the system's capabilities will likely expand, offering even more advanced and versatile solutions for enhancing and reconstructing environmental images. With each iteration, we move closer to unlocking the full potential of deep learning in transforming raw environmental data into valuable insights and actionable information.
[00054] Embodiments of the disclosure provide a method 200 for enhancing and reconstructing environmental images using deep learning. Pictorial depiction of FIG. 2, represents a flow diagram of the method 200 that can comprise steps of (at step 202) receiving an environmental image, (at step 204) normalizing and segmenting the image into relevant components, (at step 206) processing the segmented components through a trained deep learning model to enhance clarity, contrast, and resolution, (at step 208) employing a generative network to reconstruct the enhanced components into a singular image, and finally (at step 210) outputting the reconstructed enhanced image. Said steps of the method 200 can be implemented selectively or collectively, sequentially or randomly or in a combination of said implementations, in accordance with the embodiments of present disclosure.
[00055] In yet another embodiment, the first step of the method 200 involves receiving an environmental image. The image can be obtained from various sources, such as cameras, satellites, drones, underwater sensors, or any other imaging device capable of capturing environmental scenes. The image may be in the form of a digital file or data stream. For instance, consider a scenario where a satellite captures an image of a forested region. The raw image is transmitted to the system for further processing.
[00056] In yet another embodiment, once the environmental image is received, said image undergoes normalization and segmentation. Normalization involves correcting for various artifacts and imperfections that may be present in the raw image data. For example, if the image exhibits lens distortion, color imbalances, or uneven exposure, the normalization process corrects said issues to ensure a more accurate representation of the scene. In our forest image example, if the satellite image suffers from color distortion due to atmospheric conditions, the normalization step corrects said colors.
[00057] Segmentation is the process of dividing the image into relevant components or regions. Said components can include objects of interest within the scene, such as trees, terrain, bodies of water, and the sky. Segmentation helps in isolating specific elements of the image for targeted enhancement. In the forest image, segmentation might identify trees, ground cover, and the sky as distinct components.
[00058] In yet another embodiment, the heart of the method 200 lies in the processing of segmented components through a trained deep learning model. The model has been trained on a vast and diverse dataset of environmental images, allowing to understand and recognize patterns, textures, and artifacts commonly found in such images. The deep learning model applies a series of transformations to the segmented components to enhance various aspects of the image, including clarity, contrast, and resolution.
[00059] In yet another embodiment, the important feature of the step is the use of multiple convolutional neural network (CNN) layers within the deep learning model. Each CNN layer is specialized to address specific environmental artifacts that may affect image quality. For example, one CNN layer may focus on reducing atmospheric haze, while another is designed to mitigate glare or shadows. The specialization enables the system to simultaneously address multiple image quality issues. In the case of the forest image, the deep learning model may enhance the clarity of individual tree leaves, reduce the haze that obscures distant objects, and improve the contrast between the trees and the sky.
[00060] In yet another embodiment, after enhancing the segmented components, the next step involves employing a generative network to reconstruct said enhanced components into a singular, cohesive image. Generative networks, such as Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs), are well-suited for the task. Said networks are capable of generating new data samples based on learned patterns and features. In our forest image example, the generative network takes the enhanced segments of the image, including the improved trees, terrain, and sky, and combines them to create a final, enhanced representation of the entire forest scene. The reconstructed image is designed to have enhanced clarity, contrast, and resolution compared to the original.
[00061] In yet another embodiment, the final step in the method involves outputting the reconstructed enhanced image. The output can take various forms, depending on the application and user requirements. The reconstructed image can be displayed on a screen, printed, or stored for reference and analysis. In practice, the system might display the enhanced forest image on a computer monitor, making accessible to users who need a clear and detailed view of the forested area. Alternatively, the method could be stored in a database for archiving or used in various applications, such as environmental monitoring, research, or decision-making.
[00062] In some embodiments, the method 200 includes a feedback loop that enhances the effectiveness over time. The loop involves cross-referencing the reconstructed enhanced image with a ground-truth image, which represents the ideal, true-to-life version of the scene. Discrepancies detected during the cross-referencing step are used to iteratively retrain the deep learning model. For instance, if the system is used to enhance underwater images of coral reefs, the ground-truth image could be one captured by a specialized underwater camera with perfect color accuracy. By comparing the enhanced image to the ground truth, the system can identify areas where further improvements are needed and update the deep learning model accordingly. The iterative process ensures that the system continually refines the enhancement capabilities.
[00063] In certain embodiments, the segmentation step is enhanced by employing saliency maps to detect regions of high importance within the environmental image. Said saliency maps highlight areas that are crucial for achieving a visually pleasing and informative representation. The system then directs more computational resources towards enhancing said detected regions of high importance. Consider the application of satellite imagery for disaster assessment. The saliency maps generated by the system might identify regions with signs of damage, such as collapsed buildings or flooding, as high-importance areas. By allocating additional computational resources to enhance said critical areas, the system ensures that the most relevant information is presented clearly, aiding in disaster response efforts.
[00064] In yet another embodiment, in scenarios where environmental conditions change over time, such as in monitoring weather patterns or tracking traffic flow, the method incorporates a recurrent neural network (RNN) component during the reconstruction step. The RNN is responsible for sequencing the enhanced components in a temporal manner to account for dynamic environmental changes over time. For instance, in a time-lapse sequence of a bustling city street captured by a surveillance camera, the RNN ensures that the reconstructed image evolves smoothly. Said RNN takes into account the dynamic movement of vehicles and pedestrians, changing lighting conditions, and other temporal variations, resulting in a coherent representation of the evolving scene.
[00065] Referring to one or more preceding embodiments, the method 200 for enhancing and reconstructing environmental images using deep learning offers a versatile and powerful approach to improving image quality and informativeness. By combining the steps of receiving, normalizing and segmenting, deep learning-based enhancement, generative reconstruction, and outputting the reconstructed enhanced image, the method addresses a wide range of applications, from remote sensing to surveillance and scientific research.
[00066] Additionally, the method's incorporation of multiple CNN layers, a feedback loop for retraining, importance-driven enhancement, and temporal sequencing with RNNs enhances the adaptability and effectiveness. The method has the potential to revolutionize how we perceive and interact with environmental images, providing clearer and more informative representations that are invaluable in various fields. With each iteration and refinement of the deep learning model, the method continues to advance, making said method a powerful tool for image enhancement and reconstruction in diverse environmental scenarios.
[00067] Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are contained in the above discussions, these should not be construed as limitations on the scope of the subject matter described herein, but rather as descriptions of features that may be specific to particular embodiments. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable sub-combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.
[00068] Fig. 3 represents a flowchart of method for enhancing and reconstructing environmental images, according to some embodiments of the present disclosure. The method for enhancing and reconstructing environmental images begins with the collection of a substantial dataset. This collection could comprise various environmental images, such as satellite photos or street view images. The quality and diversity of these collected images play a pivotal role in determining the success of the later stages.
[00069] Once the dataset is amassed, the next phase is data preprocessing. This stage is critical as it prepares the raw images for the subsequent processing steps. The data undergoes cleaning to remove any corrupted or irrelevant images that might skew the results. Alongside this, normalization is performed to standardize the scale of the images, which aids in a smoother training process later on.
[00070] After preprocessing, the dataset is split into distinct sets for training and testing. This segregation ensures that the model is not only trained on a comprehensive dataset but also evaluated on unseen data to gauge its performance. An additional validation set might be carved out from the training data to refine model hyperparameters and mitigate overfitting risks.
[00071] The core of this method is the Deep Convolutional Neural Network (DCNN) model. The DCNN, with its multiple convolutional layers, is adept at recognizing intricate patterns in images, making it a prime candidate for tasks such as image enhancement and reconstruction. Once this model is built, it is used to extract critical features from the environmental images. These features hold the essence of the images and serve as the foundation for enhancement.
[00072] Feature enhancement then focuses on refining these extracted features. Techniques might range from sharpening to contrast enhancement, aiming to accentuate the images' crucial details. With the enhanced features at hand, the DCNN is trained to convergence. This means the training continues until the model achieves its peak performance, having learned the dataset's nuances.
[00073] However, during this training, it's paramount to monitor the model's error rate. If the error rates are undesirable, the network's parameters or even its architecture might need adjustments. These parameters, be it the learning rate or batch size, are fine-tuned to optimize the DCNN's performance further.
[00074] The final step in this method is the development of a classification model. After the images have been enhanced and reconstructed, they are classified into categories, perhaps based on attributes like clarity or quality. This classification ensures that each processed image is categorized aptly, wrapping up the comprehensive approach to enhancing and reconstructing environmental images using deep learning.
[00075] Spatial data from 2017 to 2020 was collected from Landsat and Sentinel satellites, focusing on various terrains and cloud-covered areas. This collection can be likened to the "Collect Dataset" step. Post-collection, the data underwent preprocessing, including the extraction of specific cloud types and scenes, aligning with the "Data Preprocessing" phase. The dataset was then split for testing and training, and the models were validated using near-real-time cloud-free images, resonating with the "Test train split and validation of the dataset" stage.
[00076] To address distortions, traditional deep learning approaches were employed, moving towards the "Build DCNN Model" phase. The network was trained until convergence to ensure optimal performance. Features of the images were then enhanced and extracted, fitting into the "Feature Enhancement" and "Feature Extraction" phases, respectively. Subsequent to training, the reconstructed images were evaluated against ground truth images, using visual observations and other evaluation metrics, associating with the "Control Error Rate" and "Network Parameter" steps. Finally, the research led to the proposal of improved models and algorithms for more effective cloud removal and image reconstruction, culminating in the "Classification Model" stage.
[00077] The term “memory,” as used herein relates to a volatile or persistent medium, such as a magnetic disk, or optical disk, in which a computer can store data or software for any duration. Optionally, the memory is non-volatile mass storage such as physical storage media. Furthermore, a single memory may encompass and in a scenario wherein computing system is distributed, the processing, memory and/or storage capability may be distributed as well.
[00078] Throughout the present disclosure, the term ‘server’ relates to a structure and/or module that include programmable and/or non-programmable components configured to store, process and/or share information. Optionally, the server includes any arrangement of physical or virtual computational entities capable of enhancing information to perform various computational tasks.
[00079] Throughout the present disclosure, the term “network” relates to an arrangement of interconnected programmable and/or non-programmable components that are configured to facilitate data communication between one or more electronic devices and/or databases, whether available or known at the time of filing or as later developed. Furthermore, the network may include, but is not limited to, one or more peer-to-peer network, a hybrid peer-to-peer network, local area networks (LANs), radio access networks (RANs), metropolitan area networks (MANS), wide area networks (WANs), all or a portion of a public network such as the global computer network known as the Internet, a private network, a cellular network and any other communication system or systems at one or more locations.
[00080] Throughout the present disclosure, the term “process”* relates to any collection or set of instructions executable by a computer or other digital system so as to configure the computer or the digital system to perform a task that is the intent of the process.
[00081] Throughout the present disclosure, the term ‘Artificial intelligence (AI)’ as used herein relates to any mechanism or computationally intelligent system that combines knowledge, techniques, and methodologies for controlling a bot or other element within a computing environment. Furthermore, the artificial intelligence (AI) is configured to apply knowledge and that can adapt it-self and learn to do better in changing environments. Additionally, employing any computationally intelligent technique, the artificial intelligence (AI) is operable to adapt to unknown or changing environment for better performance. The artificial intelligence (AI) includes fuzzy logic engines, decision-making engines, preset targeting accuracy levels, and/or programmatically intelligent software.

Claims
I/We Claim:
1. A system for enhancing and reconstructing environmental images using deep learning, comprising:
an image input interface configured to receive an environmental image;
a preprocessing engine configured to normalize and segment said environmental image into relevant components;
a deep learning-based enhancement module trained on a plurality of environmental image datasets, configured to apply a series of transformations to said segmented components to enhance clarity, contrast, and resolution;
a reconstruction unit incorporating generative network architectures to rebuild the segmented components into a singular enhanced image; and
an output module configured to display and store the reconstructed enhanced image.
2. The system of claim 1, wherein the deep learning-based enhancement module comprises multiple convolutional neural networks (CNN) layers, each layer being specialized to target specific environmental artifacts such as haze, glare, or shadows.
3. The system of claim 1, further comprising:
a feedback loop wherein the reconstructed enhanced image is cross-referenced with a ground-truth image, and discrepancies are utilized to retrain the deep learning-based enhancement module.
4. The system of claim 1, wherein the preprocessing engine is further configured to detect regions of high importance using saliency maps, thereby directing more computational resources towards enhancing those regions.
5. The system of claim 1, wherein the reconstruction unit further comprises a recurrent neural network (RNN) component, configured to sequence the reconstructed components in a temporal manner to account for dynamic environmental changes over time.
6. A method for enhancing and reconstructing environmental images using deep learning, comprising the steps of:
receiving an environmental image;
normalizing and segmenting the image into relevant components;
processing the segmented components through a trained deep learning model to enhance clarity, contrast, and resolution;
employing a generative network to reconstruct the enhanced components into a singular image; and
outputting the reconstructed enhanced image.
7. The method of claim 6, further comprising:
applying multiple convolutional neural network (CNN) layers during the enhancement step, with each layer targeting specific environmental artifacts such as haze, glare, or shadows.
8. The method of claim 6, further comprising:
cross-referencing the reconstructed enhanced image with a ground-truth image; and
utilizing discrepancies detected during the cross-referencing step to iteratively retrain the deep learning model.
9. The method of claim 6, further comprising:
during the segmentation step, employing saliency maps to detect regions of high importance in the environmental image; and
directing more computational resources towards enhancing the detected regions of high importance.
10. The method of claim 6, further comprising:
using a recurrent neural network (RNN) during the reconstruction step, sequencing the enhanced components in a temporal manner to account for dynamic environmental changes over time.

DESIGN OF A DEEP LEARNING-BASED MODEL TO ENHANCE AND RECONSTRUCT THE ENVIRONMENTAL IMAGE
Abstract
The disclosure introduces a sophisticated system for the enhancement and reconstruction of environmental images through deep learning methodologies. The system consists of an image input interface adept at collecting environmental images, paired with a preprocessing engine designed to normalize and divide the image into pivotal components. Building on the foundation, a uniquely trained enhancement module, ingrained with deep learning capabilities and grounded in extensive environmental image datasets, performs transformative operations on the segmented components, intensifying their clarity, contrast, and resolution. In the subsequent phase, a reconstruction unit, fortified with generative network frameworks, meticulously assembles said enhanced segments, culminating in a singular, augmented image. Completing the system, an output module is provisioned to both exhibit and archive the resultant improved image, marking a trailblazing advancement in environmental image processing. , Claims:Claims
I/We Claim:
1. A system for enhancing and reconstructing environmental images using deep learning, comprising:
an image input interface configured to receive an environmental image;
a preprocessing engine configured to normalize and segment said environmental image into relevant components;
a deep learning-based enhancement module trained on a plurality of environmental image datasets, configured to apply a series of transformations to said segmented components to enhance clarity, contrast, and resolution;
a reconstruction unit incorporating generative network architectures to rebuild the segmented components into a singular enhanced image; and
an output module configured to display and store the reconstructed enhanced image.
2. The system of claim 1, wherein the deep learning-based enhancement module comprises multiple convolutional neural networks (CNN) layers, each layer being specialized to target specific environmental artifacts such as haze, glare, or shadows.
3. The system of claim 1, further comprising:
a feedback loop wherein the reconstructed enhanced image is cross-referenced with a ground-truth image, and discrepancies are utilized to retrain the deep learning-based enhancement module.
4. The system of claim 1, wherein the preprocessing engine is further configured to detect regions of high importance using saliency maps, thereby directing more computational resources towards enhancing those regions.
5. The system of claim 1, wherein the reconstruction unit further comprises a recurrent neural network (RNN) component, configured to sequence the reconstructed components in a temporal manner to account for dynamic environmental changes over time.
6. A method for enhancing and reconstructing environmental images using deep learning, comprising the steps of:
receiving an environmental image;
normalizing and segmenting the image into relevant components;
processing the segmented components through a trained deep learning model to enhance clarity, contrast, and resolution;
employing a generative network to reconstruct the enhanced components into a singular image; and
outputting the reconstructed enhanced image.
7. The method of claim 6, further comprising:
applying multiple convolutional neural network (CNN) layers during the enhancement step, with each layer targeting specific environmental artifacts such as haze, glare, or shadows.
8. The method of claim 6, further comprising:
cross-referencing the reconstructed enhanced image with a ground-truth image; and
utilizing discrepancies detected during the cross-referencing step to iteratively retrain the deep learning model.
9. The method of claim 6, further comprising:
during the segmentation step, employing saliency maps to detect regions of high importance in the environmental image; and
directing more computational resources towards enhancing the detected regions of high importance.
10. The method of claim 6, further comprising:
using a recurrent neural network (RNN) during the reconstruction step, sequencing the enhanced components in a temporal manner to account for dynamic environmental changes over time.