DESC:FIELDOFTHEINVENTION The present disclosure relates to defective pixel detection, and more particularly relates to a method and a system for correcting defective pixels in image data. BACKGROUND OF THE INVENTION Any infrared (IR) sensor is a combination of thousands of photodetector elements generally referred to as a focal plane array (FPA). These photodetector elements sense IR radiation and go through photoelectric conversion. The output of the photodetector element is an intensity value, also called ‘pixel’. FPA is essentially an array of pixels, where each pixel represents the intensity value of IR radiation collected by the corresponding photodetector. Generally, these intensity values are proportional to the IR radiation that falls on photodetectors. FPA contains thousands or lakhs of photodetectors. Because of the manufacturing of a large number of photodetectors, even with a 99% yield, there is a chance for photodetector elements to be defective. These defective photodetectors are also termed as defective pixels. The intensity value produced by defective pixels can be either constant or can vary according to the incident light. The constant defective pixels always produce a high-intensity value or low-intensity value. High-intensity value pixel is called as “bright pixel” and the low-intensity value pixel is called as “dead pixel”. The varying defective pixels produce values may vary randomly and may not be corrected easily because of the randomness. Further, the existing methods for detecting defective pixels have many drawbacks. Many of these methods rely on the offset table, gain table method, or a user threshold based upon which the pixel’s defectiveness and non-defectiveness are decided. For example, if the pixel values are above and below a certain threshold of the mean of the offset table or gain table, such pixels are treated as defective pixels. In the extreme value method, the pixel values are directly compared with a mean of the original image. Pixel values that are above and below the certain threshold of the mean of the original image, are considered as defective pixels. In another example, an existing method relies on comparing each pixel of the image data with left and right pixels individually or with an average of left and right pixels. Further, based on a user threshold, the pixel’s defectiveness/non-defectiveness is decided. Similarly, in another existing method, the pixel data of a current line are compared with both previous line and current line pixel data. Accordingly, the correction is done based on the comparison by taking an average of the neighborhood pixels by replacing them with defective pixels. In another example, an existing method relies on correcting the defective pixels using the nearest neighbors by replacing them with an average of the neighboring pixels. Similarly, another method compares the pixel value with the surrounding pixels. If the pixel value is more than all surrounding pixels, then it is replaced with the maximum value of pixels in those neighborhood pixels. If the pixel value is less than all surrounding pixels, then that pixel is replaced with the minimum value of pixel. In another example, an existing method deals with the correction of a cluster of defective pixels by replacing the defective pixel with the average of the nearest valid pixel pair or by applying a linear interpolation method on the nearest non-defective neighbors. Thus, the defective pixels can be either single location pixels or cluster(s) of pixels. In prior arts, several methods were introduced for the correction of single-location defective pixels and the cluster of defective pixels. However, in the existing methods, there is a limitation over the search region to find the nearest non-defective pixel. Hence, there is a need for a technique to overcome the above-discussed problems. SUMMARY This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention. This summary is neither intended to identify key or essential inventive concepts of the invention nor is it intended for determining the scope of the invention. According to one embodiment of the present disclosure, a method for correcting defective pixels in image data is provided. The method includes determining one or more defective pixels amongst a plurality of pixels in the image data. The method includes identifying a first location corresponding to each of the one or more defective pixels. The method further includes determining at least one nearest non-defective pixel amongst the plurality of pixels for each of the one or more defective pixels based on the identified first location corresponding to each of the one or more defective pixels using a region-growing scanning technique. The method further includes identifying a second location associated with the at least one nearest non-defective pixel for each of the one or more defective pixels in the image data. The method further includes correcting the determined one or more defective pixels by replacing the one or more defective pixels located at the corresponding first location with the corresponding at least one nearest non-defective pixel located at the second location. According to one embodiment of the present disclosure, a system for correcting defective pixels in image data is provided. The system includes at least one processor in communication with a memory. The at least one processor is configured to determine one or more defective pixels amongst a plurality of pixels in the image data. The at least one processor is configured to identify a first location corresponding to each of the one or more defective pixels. The at least one processor is configured to determine at least one nearest non-defective pixel amongst the plurality of pixels for each of the one or more defective pixels based on the identified first location corresponding to each of the one or more defective pixels using a region-growing scanning technique. The at least one processor is configured to identify a second location associated with the at least one nearest non-defective pixel for each of the one or more defective pixels in the image data. The at least one processor is further configured to correct the determined one or more defective pixels by replacing the one or more defective pixels located at the corresponding first location with the corresponding at least one nearest non-defective pixel located at the second location. To further clarify the advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which are illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail in the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS These and other features, aspects, and advantages of the present invention will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein: Figure 1 illustrates an environment for the implementation of a system for correcting defective pixels in image data, according to an embodiment of the present invention; Figure2 illustrates a process flow of a detection module of a processor of the system (of Figure 1) for detection of defective pixels, according to an embodiment of the present invention; Figures 3a, 3b, and 3c illustrate exemplary scenarios of a growing neighborhood scanning for identifying non-defective pixels near the defective pixels, according to an embodiment of the present invention; Figure 4 illustrates a detailed process flow of a method for correcting defective pixels in image data, according to an embodiment of the present invention; Figure 5a illustrates a detailed flowchart of a process for the detection of defective pixels, according to an embodiment of the present invention; Figure 5b illustrates a detailed flowchart of a process 500b, which is in continuation with the process 500a, for generation of a defective pixel map, according to an embodiment of the present invention; and Figure 5c illustrates a detailed flowchart of a process 500c, which is in continuation with the process 500b, of a region-growing scanning method for generation of a defective pixels correction list, according to an embodiment of the present invention. It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative methods embodying the principles of the present disclosure. Similarly, it will be appreciated that any flow charts, flow diagrams, and the like represent various processes which may be substantially represented in computer-readable medium and so executed by a computer or processor, whether or not such computer or processor is explicitly shown. DETAILED DESCRIPTION For the purpose of promoting an understanding of the principles of the present disclosure, reference will now be made to the various embodiments and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the present disclosure is thereby intended, such alterations and further modifications in the illustrated system, and such further applications of the principles of the present disclosure as illustrated therein being contemplated as would normally occur to one skilled in the art to which the present disclosure relates. It will be understood by those skilled in the art that the foregoing general description and the following detailed description are explanatory of the present disclosure and are not intended to be restrictive thereof. Whether or not a certain feature or element was limited to being used only once, it may still be referred to as “one or more features” or “one or more elements” or “at least one feature” or “at least one element.” Furthermore, the use of the terms “one or more” or “at least one” feature or element does not preclude there being none of that feature or element, unless otherwise specified by limiting language including, but not limited to, “there needs to be one or more…” or “one or more elements is required.” Reference is made herein to some “embodiments.” It should be understood that an embodiment is an example of a possible implementation of any features and/or elements of the present disclosure. Some embodiments have been described for the purpose of explaining one or more of the potential ways in which the specific features and/or elements of the proposed disclosure fulfill the requirements of uniqueness, utility, and non-obviousness. Use of the phrases and/or terms including, but not limited to, “a first embodiment,” “a further embodiment,” “an alternate embodiment,” “one embodiment,” “an embodiment,” “multiple embodiments,” “some embodiments,” “other embodiments,” “further embodiment”, “furthermore embodiment”, “additional embodiment” or other variants thereof do not necessarily refer to the same embodiments. Unless otherwise specified, one or more particular features and/or elements described in connection with one or more embodiments may be found in one embodiment, or may be found in more than one embodiment, or may be found in all embodiments, or may be found in no embodiments. Although one or more features and/or elements may be described herein in the context of only a single embodiment, or in the context of more than one embodiment, or in the context of all embodiments, the features and/or elements may instead be provided separately or in any appropriate combination or not at all. Conversely, any features and/or elements described in the context of separate embodiments may alternatively be realized as existing together in the context of a single embodiment. Any particular and all details set forth herein are used in the context of some embodiments and therefore should not necessarily be taken as limiting factors to the proposed disclosure. The terms “comprises”, “comprising”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such process or method. Similarly, one or more devices or sub-systems or elements or structures or components proceeded by “comprises... a” does not, without more constraints, preclude the existence of other devices or other sub-systems or other elements or other structures or other components or additional devices or additional sub-systems or additional elements or additional structures or additional components. The term “couple” and the derivatives thereof refer to any direct or indirect communication between two or more elements, whether or not those elements are in physical contact with each other. The terms “transmit”, “receive”, and “communicate” as well as the derivatives thereof encompass both direct and indirect communication. The term “or” is an inclusive term meaning “and/or”. The phrase “associated with,” as well as derivatives thereof, refer to include, be included within, interconnect with, contain, be contained within, connect to or with, coupled to or with, be communicable with, cooperate with, interleave, juxtapose, be proximate to, be bound to or with, have, have a property of, have a relationship to or with, or the like. The term “controller” refers to any device, system, or part thereof that controls at least one operation. The functionality associated with any particular controller may be centralized or distributed, whether locally or remotely. The phrase “at least one of,” when used with a list of items, means that different combinations of one or more of the listed items may be used, and only one item in the list may be needed. For example, “at least one of A, B, and C” includes any of the following combinations: A, B, C, A and B, A and C, B and C, and A and B and C, and any variations thereof. As an additional example, the expression “at least one of a, b, or c” may indicate only a, only b, only c, both a and b, both a and c, both b and c, all of a, b, and c, or variations thereof. Similarly, the term “set” means one or more. Accordingly, the set of items may be a single item or a collection of two or more items. Moreover, multiple functions described below may be implemented or supported by one or more computer programs, each of which is formed from computer-readable program code and embodied in a computer-readable medium. The terms “application” and “program” refer to one or more computer programs, software components, sets of instructions, procedures, functions, objects, classes, instances, related data, or a portion thereof adapted for implementation in a suitable computer-readable program code. The phrase “computer readable program code” includes any type of computer code, including source code, object code, and executable code. The phrase “computer-readable medium” includes any type of medium capable of being accessed by a computer, such as Read Only Memory (ROM), Random Access Memory (RAM), or any other type of memory. A “non-transitory” computer-readable medium excludes wired, wireless, optical, or other communication links that transport transitory electrical or other signals. A non-transitory computer-readable medium includes media where data may be permanently stored and media where data may be stored and later overwritten, such as a rewritable optical disc or an erasable memory device. The various embodiments of the present disclosure relate to a system and a method for detection and correction of defective pixels in IR imaging sensors. Figure 1 illustrates an environment 100 for the implementation of a system 130 for correcting defective pixels in image data 120, according to an embodiment of the present invention. In a non-limiting example, the environment 100 may include an imaging device 110 which may further include a power supply 101, an optics 102, a mechanical shutter 103, an Infrared (IR) sensor 104, and a control unit 105. In an implementation, the imaging device 110 may be powered by the power supply 101, which may provide the required electrical energy to both the IR sensor 104 and the control unit 105. The control unit 105 may help in controlling the mechanical shutter 103 and sending control signals to the IR sensor 104 and the system 130. The IR sensor 104 of the imaging device 110 may be configured for detecting the IR radiation. For instance, the IR sensor 104 may detect the IR radiation emitted from an object or surroundings and convert heat data associated with the detected IR radiation into a corresponding electronic signal that may be processed further. The control unit 105 may be configured for managing the IR sensor’s operation and may process the electronic signal into a usable form, such as the image data 120. The image data 120 produced by the image device 110 may comprise a plurality of pixels 123. Each of the plurality of pixels 123 is the smallest unit that may represent a single point in the image data 120. Each pixel may detect light or IR radiation and contribute to the overall image data 120. The plurality of pixels 123 may comprise one or more defective pixels (D) 122. The defective pixels (D) 122 refer to individual pixels that fail to function correctly, which may be due to manufacturing flaws or damage during use. For instance, the IR sensor 104 of the imaging device110 may be used in focal plane arrays (FPAs). In a non-limiting example, one or more IR sensors 104 may be integrated onto a single board. There may be a likelihood that at least one IR sensor 104 is defective. The defective IR sensor 104 may result from manufacturing flaws or long-term exposure to extreme environments during usage, which may cause malfunction. The defective IR sensor 104 may lead to the creation of the defective pixels (D) 122, which may negatively impact the quality of the image data 120. The defective pixels (D) 122 may appear as a persistent black or white spot. The defective pixels (D) 122 may not respond to IR radiation properly, leading to inaccurate image data 120. In a non-limiting example, the defective pixels (D) 122 may be, but are not limited to, “dead” pixels that do not emit any signal, or “hot” pixels that may be always active, resulting in bright spots in the image data 120. The defects may occur due to physical damage, temperature fluctuations, or imperfections in the IR sensor's production process. The mechanical shutter 103 may be positioned between the IR sensor 104 and the optics 102 to regulate the exposure of the IR sensor 104 to the incoming IR radiation. The mechanical shutter 103 may be controlled by the control unit 105. The control unit 105 may open or close the mechanical shutter 103, as needed, to control the amount of IR radiation reaching the IR sensor 104, ensuring optimal imaging conditions. Together, the IR sensor 104, the control unit 105, the mechanical shutter 103, and the power supply 101 work in tandem to capture the image data 120 while maintaining the proper exposure and protection for the IR sensor 104. The defective pixels (D) 122 may be processed by the system 130 to detect and correct one or more defective pixels 122 present in the image data 120. The system 130 may include at least one processor 132, memory 134, and a data unit 136. The processor 132 may include an in-build memory (not shown in Figure 1) which may comprise a detection module 132a and a correction module 132b. In an implementation, the system 120 may be the part of the imaging device 110. In an implementation, the system 120 may be coupled with the imaging device 110. In an embodiment, the system 130 may be configured to detect the one or more defective pixels (D) 122 amongst the plurality of pixels 123 and correct the detected defective pixels (D) 122. The system, upon detecting the defective pixels (D) 122, may replace the defective pixels with one or more values from the nearest neighboring non-defective pixels, ensuring that the overall image quality remains consistent and accurate despite the presence of defects. In an embodiment, the at least one processor 132 may be in communication with the memory 134. The at least one processor 132 may be configured to initiate or stop one or more routines or a process based on the image data 120 received by the system 130 from the imaging device 110. The at least one processor 132 may be a single processing unit or several units, all of which could include multiple computing units. The at least one processor 132 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the at least one processor 132 may be configured to fetch and execute computer-readable instructions and data stored in the memory 134. In an embodiment, the memory 134 may include any non-transitory computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read-only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. In an embodiment, the data unit 136 amongst other things, includes routines, programs, objects, components, data structures, etc., which perform particular tasks or implement data types. The data unit 136 may also be implemented as signal processor(s), state machine(s), logic circuitries, and/or any other device or component that manipulates signals based on operational instructions. Further, the data unit 136 may be implemented in hardware, instructions executed by a processing unit, or by a combination thereof. The processing unit may comprise a processor, such as the at least one processor 132, a state machine, a logic array, or any other suitable devices capable of processing instructions. The processing unit may be a general-purpose processor which executes instructions to cause the general-purpose processor to perform the required tasks or, the processing unit can be dedicated to performing the required functions. In another embodiment of the present disclosure, the data unit 136 may be machine-readable instructions (software) that, when executed by the processor 126, perform any of the described functionalities. In an embodiment, the various units/components 134-136 may be a part of the at least one processor 132. In another embodiment, the at least one processor 132 may be configured to perform the functions of detection module 132a and correction module 132b. The data unit 136 may serve, amongst other things, as a repository for storing data processed, received, and generated signals from the modules 132a-132b of the at least one processor 132, and the memory 134. In an embodiment, the at least one processor 132 may be configured to determine the one or more defective pixels (D) 122 amongst the plurality of pixels 123 in the image data 120. Figure 2 illustrates a process flow of the detection module 132a of the at least one processor 132 of the system 130 for the detection of defective pixels, according to an embodiment of the present invention. In an embodiment, at step 202, the detection module 132a may be configured to determine a mean value (x1) and a standard deviation value (x2) of the plurality of pixels 123 in the image data 120. x1=1/MN ?_(i=1)^M¦?_(j=1)^N¦?F(i,j)? (1) x2=sqrt(1/MN ?_(i=1)^M¦?_(j=1)^N¦??|F(i,j)-x1|?^2)? (2) At step 204, the detection module 132a may be configured to determine a first threshold value and a second threshold value based at least on the mean value (x1) and the standard deviation value (x2) of the plurality of pixels. In an implementation, the first threshold value may be a lower threshold value (Thl) and the second threshold value may be an upper threshold value (Thu). The determining of the first threshold value (Thl) and the second threshold value (Thu) may be based on a correlation of the mean value (x1), the standard deviation value (x2) of the plurality of pixels 123, and a user parameter (n). In a non-limiting example, the user parameter (n) may be manually set/reset. The first threshold value (Thl) and the second threshold value (Thu) may be correlated as given below in equations (3) and (4), respectively: ?Th?_l=x1-n*x2 (3) ?Th?_u=x1+n*x2 (4) At step 206, the detection module 132a may be configured to compare a pixel value associated with each of the plurality of pixels 123 with the first threshold value (Thl) and the second threshold value (Thu). In an implementation, the each pixel value associated with the pixel 123 under test in a current frame may be compared with the first threshold value (Thl) and the second threshold value (Thu). At step 208, the detection module 132a may be configured to generate a first output indicating the defective pixel (D) 122 when a corresponding pixel value is above the first threshold value or below the second threshold value. At step 210, the detection module 132a may be configured to generate a second output indicating the non-defective pixel when the corresponding pixel value is within a range defined by the first threshold value and the second threshold value. In an implementation, based on the comparison in step 206, either the first output or the second output may be generated, wherein the first output may be referred to as “1” and the second output may referred to as “0”. The one or more pixel locations for which the output is “1” may be treated as the defective pixel locations (D) 122, which may be stored in the memory 134. Referring to Figure 1, in an embodiment, the at least one processor 132 may be configured to identify a first location corresponding to each of the one or more defective pixels (D) 122. The first location may be a pixel location corresponding to the one or more defective pixels (D) 122. Figures 3a, 3b, and 3c illustrate exemplary scenarios of a region-growing scanning for identifying non-defective pixels near the defective pixels, according to an embodiment of the present invention. In an embodiment, the at least one processor 132 may be configured to determine at least one nearest non-defective pixel amongst the plurality of pixels 123 for each of the one or more defective pixels (D) 122 based on the identified first location corresponding to each of the one or more defective pixels using the region-growing scanning technique. Referring to Figure 3, the region-growing scanning technique is an image segmentation technique used for grouping neighboring pixels that share similar properties, such as intensity or color. In a non-limiting example, the region-growing scanning technique may initiate with a seed pixel or voxel, which may be selected either manually or automatically. For instance, the defective pixels (D) 122 may be considered as the seed pixel. The region may expand to adjacent pixels (P1-P8 as shown in Figure 3a) that meet certain criteria, such as having similar intensity values within a defined region of interest. The growth continues iteratively, where new pixels may be added to the region (for example, Q1-Q16 as shown in Figure 3b, and R1-R24 as shown in Figure 3c) if they satisfy the similarity condition, effectively segmenting the image into homogeneous regions. The region-growing scanning technique may help in segmenting regions with uniform texture or intensity. In an exemplary implementation, Figure 3a shows the region-growing scanning technique for a neighborhood window size of one (k=1). The defective pixel may be represented by ‘D’. The method may firstly search for non-defective pixels in left, right, up and down (P1, P2, P3, P4) locations of the defective pixel D. If none of the pixels are non-defective, then the search may continue for the next nearest neighbors, which are corner pixels for k=1. These pixels may be represented as P5, P6, P7, and P8. If no non-defective pixel location is found, then the scanning region may increase neighborhood size to two. In another exemplary implementation, Figure 3b shows the region-growing scanning technique with the neighborhood window size of two (k=2). For the defective pixel ‘D’, the region-growing scanning technique may, firstly search for non-defective pixels at left, right, top, and bottom locations (Q1, Q2, Q3, Q4). The next nearest pixel locations may be bottom-left, bottom-right, top-left, and top-right. Thus, the region-growing scanning technique may search for non-defective pixels at bottom-left (Q5, Q6), bottom-right (Q7, Q8), top-left (Q9, Q10) and top-right (Q11, Q12) locations. For k=2, corners may be the next nearest pixel locations. If none of the pixels are found to be non-defective at corner pixel locations (Q13, Q14, Q15, Q16), then neighborhood size may be increased to three (k=3). In another exemplary implementation, Figure 3c shows the region-growing scanning technique for k=3. The region-growing scanning technique may search in the same way as mentioned above, where the region-growing scanning technique may look for non-detective pixels at top, down, left, and right locations and then move away from these locations towards corners and finally search at corners. After the scan method has finished, the locations of all defective pixels along with corresponding non-defective pixels are stored in the memory 134, which may be a non-volatile memory (NVM). In an embodiment, the region-growing scanning technique may include determining the plurality of neighboring pixels amongst the plurality of pixels 123 around the neighboring region of the defective pixel (D) 122 based on the corresponding first location. The plurality of neighboring pixels (P1-P8) may be selected from one or more adjacent pixels within a predefined distance around the region of the defective pixel. The region-growing scanning technique may further include expanding the neighboring region of the deflective pixel (D) 122 by iteratively evaluating the plurality of neighboring pixels associated with the newly added region (Q1-Q16, R1-R24) for forming a continuous region, wherein expanding the region is based on a predefined condition. The region-growing scanning technique may further include terminating the expansion of the neighboring region of the deflective pixel (D) 122 based on one or more criteria. For instance, the predefined condition may be based on a condition that if no non-defective pixel is found in a MxM region, only then the region size will expand to (M+1) x (M+1) or if at least one non-defective pixel is found in MxM region, region size will not expand. Thus, if a predetermined size limit, that is, a maximum number of rows or columns in the image data 120, of the neighboring region is reached, the expansion of the neighboring region may terminate. Once the location of the non-defective pixels for the corresponding defective pixels (D) 122 is identified, the same may be stored in the memory 134. Similarly, the location of defective pixels (D) 122 may also be stored in the memory 134. The stored locations of non-defective pixels and defective pixels (D) 122 may be used for correction of the image data 120. In an implementation, the termination of the expansion of the neighboring region based on the one or more criteria may include a condition when at least one nearest non-defective pixel amongst the plurality of pixels around the region of the defective pixel is determined. In an implementation, the termination of the expansion of the neighboring region based on the one or more criteria may include another condition when no further neighboring pixels satisfy the predefined condition. In an implementation, the termination of the expansion of the neighboring region based on the one or more criteria may include another condition when a predefined size limit of the neighboring region is reached. In an embodiment, the at least one processor 132 may be configured to identify a second location associated with the at least one nearest non-defective pixel for each of the one or more defective pixels in the image data 120. In an embodiment, the at least one processor 132 may be configured to correct the determined one or more defective pixels by replacing the one or more defective pixels located at the corresponding first location with the corresponding at least one nearest non-defective pixel located at the second location. In an embodiment, the user parameter (n) may be adapted to be adjusted if the one or more defective pixels (D) 122 persist in the image data 120 after correcting the defective pixel by replacing the defective pixel (D) 122 at the first location with the nearest non-defective pixel from the second location in the memory 136. Figure 4 illustrates a flow chart of method 400 for correcting defective pixels in image data, according to an embodiment of the present invention. The method 400 may include receiving the image data 120 from the imaging device 110. In a non-limiting example, the image data 120 may be the plurality of pixels 123 which may further comprise the one or more defective pixels (D) 122 and the non-defective pixels. At step 402, the method 400 may include determining one or more defective pixels amongst a plurality of pixels in the image data. In an implementation, to determine the one or more defective pixels, the method 400 may capture two consecutive set of frames of pixels from the image data 120. The two consecutive sets of frames of pixels herein and after may be referred to as a previous frame and a current frame. In an embodiment, the method 400 may determine the standard deviation by correlating a global mean of the previous frame, and a global variance which may be determined based on a mean of the current frame. In an implementation, the first threshold (?Th?_l) may be calculated by adding the standard deviation multiplied by the userparameter (n) to the global mean of the previous frame. In an implementation the second threshold (?Th?_u) may be calculated by subtracting the standard deviation (x2) multiplied by the user parameter (n) from the global mean of the previous frame. In an embodiment, the method 400 may compare the each pixel corresponding to the current frame with the first threshold value (?Th?_l) and the second threshold value (?Th?_u). Through comparing, either the first output (referred to earlier as “1” for defective pixels (D) 122) or the second output (referred to earlier as “0” for non-defective pixels). At step 404, the method 400 may include identifying a first location corresponding to each of the one or more defective pixels (D) 122. In an implementation, the pixel location with pixel value “1” may be identified as the defective pixels (D) 122. At step 406, the method 400 may include determining at least one nearest non-defective pixel amongst the plurality of pixels for each of the one or more defective pixels based on the identified first location corresponding to each of the one or more defective pixels using a region-growing scanning technique. At step 408, the method 400 may include identifying a second location associated with the at least one nearest non-defective pixel for each of the one or more defective pixels in the image data. At step 410, the method 400 may include correcting the determined one or more defective pixels by replacing the one or more defective pixels located at the corresponding first location with the corresponding at least one nearest non-defective pixel located at the second location. In an implementation, the method 400 may further include storing the first location corresponding to each of the one or more defective pixels (D) 122 and the second location associated with the at least one nearest non-defective pixel corresponding to each of the one or more deflective pixels (D) 122 in the memory 134. Figure 5a illustrates a detailed flowchart of a process 500a for detection of defective pixels, according to an embodiment of the present invention. Figure 5b illustrates a detailed flowchart, which is in continuation with the process 500a, of a process 500b for generation of a defective pixel map, according to an embodiment of the present invention. Figure 5c illustrates a detailed flowchart, which is in continuation with the process 500b, of a process 500c of region-growing scanning method for generation of a defective pixels correction list, according to an embodiment of the present invention. The steps 501-511 may correspond to step 402 of the method 400, that is determining one or more defective pixels (D) 122 amongst a plurality of pixels 123 in the image data 120. Referring to Figure 5a, at step 501, the process 500a may of detecting defective pixels (D) 122 may be initialized. Three variables i, j, and k may be initialized as i =0, j=1, and k=0. The variable i may represent an iteration count related to the pixel in an image frame, the variable j may represent a frame index used for a comparison process and the variable k may represent an iteration count related to the pixel in an image frame for identifying the defective/non-defective pixels. In an implementation, a current frame (Xi) from the image data 120 may be retrieved by the system 130 from the memory 134. At step 502, the mean of the previous frame (Xpm) may be calculated. The mean of the previous frame (Xpm) may be computed using Xmj-1which may be used for comparing the current frame with the previous frames. Xpm = Xmj-1 (5) At step 503, the mean of the current frame (Xcm) may be computed as: Xcm = ?X_i/N (6) At step 504, the variance (X1) may be computed as follows: X1=X1 + (Xcm-Xi)^2/N (7) After step 504, the condition of step 505 may be checked if all the pixels in image frames (i <= N) are processed. If the above condition satisfies, i.e., i is less than equal to N, i may be incremented by 1, and the process may reiterate at step 505. Otherwise, if the above condition is not satisfied, i.e., i is not less than equal to N, the process may proceed to next step 506. At step 506, the standard deviation (x2) may be derived from the variance (X1) as follows: x2 = vX1 (8) At step 507, the first threshold value (?Th?_l)and the second threshold value (?Th?_u) may be calculated as follows: (?Th?_l )=Xpm-X2 (9) (?Th?_u )=Xpm+X2 (10) The first threshold value (?Th?_l)and the second threshold value (?Th?_u)may be used in determining significant frame changes. At step 508, the current frame intensity may be checked as follows: (?Th?_l )