Image signal processor and method for processing image signal

Abstract

The invention relates to an image signal processor and a method for processing an image signal. An image signal processor includes: a threshold calculator configured to calculate a threshold of a target pixel based on a complexity of a target core including the target pixel; an adjacent pixel determiner configured to determine, when the target pixel is a corner pixel, one or more effective pixels of the target kernel and at least one reference pixel located outside the target kernel as adjacent pixels of the target pixel; and a defect detector configured to determine whether the target pixel is a defective pixel according to a result of comparing a difference between pixel data of an adjacent pixel and pixel data of the target pixel with a threshold, where the corner pixel is a pixel located at each vertex of the target kernel.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This patent document claims priority to and the benefit of Korean Patent Application No. 10-2023-0078878, filed on June 20, 2023, the disclosure of which is incorporated herein by reference in its entirety as a part of the disclosure of this patent document. Technical Field

[0003] The technology and embodiments disclosed in this patent document generally relate to an image signal processor capable of correcting defective pixels and an image signal processing method thereof. Background Art

[0004] Image sensing devices are devices for capturing optical images by converting light into electrical signals using photosensitive semiconductor materials that react to light. With the development of the automotive, medical, computer, and communications industries, the demand for high-performance image sensing devices is increasing in various fields such as smartphones, digital cameras, game consoles, IoT (Internet of Things), robots, surveillance cameras, and medical micro cameras.

[0005] The original image captured by the image sensing device may include defects in the original image. Such captured images may also include defective pixels, i.e., pixels that do not emit light corresponding to the original image due to temporary factors. Since images containing defective pixels will result in degraded image quality, a process for correcting defective pixels in an image would be an improvement over the prior art. The location of the defective pixels may change randomly, and the quality of the image may be improved as the detection accuracy of the defective pixels is improved. Summary of the invention

[0006] Various embodiments of the disclosed technology relate to an image signal processor and an image signal processing method thereof that can more accurately detect defective pixels.

[0007] According to an embodiment of the disclosed technology, an image signal processor may include: a threshold calculator configured to calculate a threshold of a target pixel based on the complexity of a target kernel including the target pixel; an adjacent pixel determiner configured to: when the target pixel is a corner pixel, determine one or more valid pixels of the target kernel and at least one reference pixel located outside the target kernel as adjacent pixels of the target pixel; and a defect detector configured to determine whether the target pixel is a defective pixel based on a result of comparing a difference between pixel data of the adjacent pixels and pixel data of the target pixel with a threshold, wherein a corner pixel is a pixel located at each vertex of the target kernel.

[0008] According to another embodiment of the disclosed technology, an image signal processor may include: a complexity calculator configured to calculate the complexity of a target kernel including a target pixel; a threshold calculator configured to: when the target pixel is a corner pixel, calculate the contrast of heterochromatic pixels adjacent to the target pixel and corresponding to a color different from that of the target pixel, and calculate a threshold of the target pixel based on the complexity and contrast; and a defect detector configured to determine whether the target pixel is a defective pixel based on a result of comparing a difference between pixel data of pixels adjacent to the target pixel and pixel data of the target pixel with a threshold, wherein a corner pixel is a pixel located at each vertex of the target kernel.

[0009] According to another embodiment of the disclosed technology, an image signal processor may include: a complexity calculator configured to calculate the complexity of a target kernel including a target pixel; a threshold calculator configured to determine whether the target pixel is a corner pixel located at a vertex of the target kernel, and to calculate a threshold of the target pixel based on the complexity; an adjacent pixel determiner configured to: when the target pixel is not a corner pixel, determine that a valid pixel of the target kernel is an adjacent pixel; and a defect detector configured to determine whether the target pixel is a defective pixel based on a result of comparing a difference between pixel data of an adjacent pixel and pixel data of a target pixel with a threshold.

[0010] It is to be understood that both the foregoing general description and the following detailed description of the disclosed technology are illustrative and explanatory and are intended to provide further explanation of the disclosure as claimed. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The above and other features and advantageous aspects of the disclosed technology will become apparent with reference to the following detailed description when considered in conjunction with the accompanying drawings.

[0012] Figure 1 is a block diagram illustrating an example of an image signal processor based on some embodiments of the disclosed technology.

[0013] Figure 2 is a flowchart illustrating an image signal processing method based on some embodiments of the disclosed technology.

[0014] Figure 3 The following are some examples based on the disclosed technology. Figure 2 A schematic diagram of an example of raw image data that is a target of an image signal processing method.

[0015] Figure 4 The present invention is shown as some embodiments based on the disclosed technology. Figure 2 A schematic diagram of another example of raw image data that is a target of an image signal processing method.

[0016] Figure 5 The present invention is shown as some embodiments based on the disclosed technology. Figure 2 A schematic diagram of still another example of raw image data that is a target of the image signal processing method.

[0017] Figure 6 is shown with Figure 1 A block diagram of an example of a computing device corresponding to an image signal processor. DETAILED DESCRIPTION

[0018] The present disclosure describes embodiments and examples of an image signal processor, a suitably programmed general purpose digital signal processor (DSP), or a suitably programmed general purpose processor (e.g., microprocessor, microcontroller), any of which is capable of correcting defective pixels in an image. It also describes an image signal processing method for detecting and correcting defective pixels. The disclosed apparatus and method are embodiments of an image signal processor and an image signal processing method that can improve the accuracy of the operation of determining whether a corner pixel in a target kernel is a defective pixel.

[0019] Reference will now be made in detail to embodiments of the disclosed technology, examples of which are shown in the accompanying drawings. Although the disclosure is susceptible to various modifications and alternative forms, specific embodiments thereof are shown by way of example in the accompanying drawings. The disclosure should not be construed as being limited to the embodiments set forth herein.

[0020] Figure 1 is a block diagram illustrating an example of an image signal processor (ISP) 100 based on some embodiments of the disclosed technology.

[0021] Reference Figure 1 , the image signal processor (ISP) 100 may perform at least one image signal processing on the image data (IDATA) to generate processed image data (IDATA_P).

[0022] The image signal processor (ISP) 100 can reduce the noise of the image data (IDATA) and can perform various image signal processing (e.g., demosaicing, defective pixel correction, gamma correction, color filter array interpolation, color matrix, color correction, color enhancement, lens distortion correction, etc.) for improving the image quality of the image data.

[0023] The image signal processor (ISP) 100 may compress image data created by performing image signal processing for image quality improvement, so that the image signal processor (ISP) 100 may create an image file using the compressed image data. Alternatively, the image signal processor (ISP) 100 may restore the image data from the image file. In this case, the scheme for compressing such image data may be a reversible format or an irreversible format. As a representative example of such a compression format, in the case of using a still image, a Joint Photographic Experts Group (JPEG) format, a JPEG2000 format, or the like may be used. In addition, in the case of using a moving image, a plurality of frames may be compressed according to the Moving Picture Experts Group (MPEG) standard, so that a moving image file may be created.

[0024] The image data (IDATA) may be generated by an image sensing device that captures an optical image of a scene, but the scope of the disclosed technology is not limited thereto. The image sensing device may include: a pixel array including a plurality of pixels configured to sense incident light received from a scene; a control circuit configured to control the pixel array; and a readout circuit configured to output digital image data (IDATA) by converting analog pixel signals received from the pixel array into digital image data (IDATA). In some embodiments of the disclosed technology, it is assumed that the image data (IDATA) is generated by the image sensing device.

[0025] The pixel array may include a color filter array (CFA) in which color filters are arranged according to a predetermined pattern (e.g., a Bayer pattern, a quad Bayer pattern, a nine Bayer pattern, an RGBW pattern, etc.) so that each color filter can sense light of a predetermined wavelength band. The pattern of the image data (IDATA) may be determined according to the type of the pattern of the CFA.

[0026] As used herein, the word "predetermined" with respect to a parameter (such as a predetermined pattern, threshold, size, distance, condition, algorithm, or wavelength or frequency band) means that the value of a particular predetermined parameter is determined before the parameter is used in a process or algorithm. For some embodiments, the value of the predetermined parameter is determined before the process or algorithm begins. In other embodiments, the value of the predetermined parameter is determined during the operation of the process or algorithm but before the parameter is used in the process or algorithm.

[0027] like Figure 1As shown, the image signal processor (ISP) 100 may include a defective pixel determiner 200 and a defective pixel corrector 300. The defective pixel determiner 200 may detect defective pixels in raw image data. Raw image data may refer to image data (IDATA) or data obtained by preprocessing the image data (IDATA), and may correspond to a specific pattern (e.g., a Bayer pattern, a quad-Bayer pattern, a nine-Bayer pattern, or an RGBW pattern, etc.). Raw image data having such a specific pattern includes pixel data corresponding to only one color per pixel. Here, pixel data may refer to image data corresponding to one pixel, and a group (or set) of pixel data corresponding to one frame may constitute raw image data.

[0028] A defective pixel may refer to a pixel that does not generate correct or accurate pixel data, an example of which is a pixel that does not generate data that accurately corresponds to the intensity of light incident on the pixel. A defective pixel may be a specific pixel according to pixel properties (e.g., a phase difference detection autofocus (PDAF) pixel, a defective pixel with defects due to manufacturing process limitations), or a defective pixel may be a pixel that is temporarily unable to generate normal (i.e., accurate) pixel data due to environmental or structural reasons. Here, a PDAF pixel may be a pixel used to obtain phase difference information to achieve an autofocus function, and may be classified as a defective pixel from the perspective of image data processing.

[0029] The defective pixel determiner 200 may include a pixel attribute extractor 210, a complexity calculator 220, a threshold calculator 230, a neighboring pixel determiner 240, and a defect detector 250, each of which is preferably implemented as a corresponding program instruction executed by a processor.

[0030] The pixel attribute extractor 210 may extract (or obtain) attribute information of a pixel corresponding to the pixel data included in the original image data. The attribute information of the pixel may include at least one of color information (e.g., red, green, and / or blue) of the corresponding pixel, information about whether the corresponding pixel is a fixed defective pixel (e.g., a PDAF pixel or a bad pixel), and information about the position of the corresponding pixel. The attribute information of the pixel may be stored in a memory (not shown, such as a line memory or a frame memory) that can be accessed by the image signal processor (ISP) 100 for each pixel.

[0031] The pixel attribute extractor 210 may extract attribute information of the target pixel. When the target pixel is a fixed defective pixel (e.g., a PDAF pixel or a bad pixel), the pixel attribute extractor 210 may control the defective pixel corrector 300 to perform defective pixel correction on the target pixel. At this time, the defective pixel determiner 200 may not determine whether the target pixel is a defective pixel.

[0032] The pixel property extractor 210 may provide pixel property information required for operations of other components 220 to 250 of the defective pixel determiner 200 .

[0033] The complexity calculator 220 may calculate the complexity of the target kernel including the target pixel. The complexity may refer to an average deviation and may be an average index indicating how much the pixel data of each pixel is dispersed (distributed) relative to the average value of the pixel data of the pixels included in the target kernel.

[0034] The threshold calculator 230 may calculate a threshold value as a criterion for determining whether the target pixel is a defective pixel. The threshold value may be calculated using the complexity calculated by the complexity calculator 220. In addition, the threshold calculator 230 may calculate the contrast of a plurality of heterochromatic pixels adjacent to the target pixel, and may calculate the threshold value using the complexity and the contrast. Here, the heterochromatic pixel may refer to a pixel corresponding to a color different from the color of the target pixel. In addition, the contrast may refer to the difference between the maximum pixel data and the minimum pixel data among the pixel data of the plurality of heterochromatic pixels.

[0035] The neighboring pixel determiner 240 may determine a range or set of neighboring pixels, the output of which will be compared with a threshold value to determine whether the target pixel is a defective pixel.

[0036] According to one embodiment, when the target pixel is a normal pixel, the adjacent pixel determiner 240 may determine the pixels included in the target kernel as the adjacent pixels. Here, the normal pixel may refer to a pixel that is not a PDAF pixel and is not used as a corner pixel located at each vertex of the target kernel having a rectangular shape.

[0037] According to another embodiment, when the target pixel is a green pixel and operates as a corner pixel, the adjacent pixel determiner 240 may determine not only pixels in the target core that are not PDAF pixels as adjacent pixels, but may also determine other pixels located in a diagonal direction of the target core and belonging to another core as adjacent pixels.

[0038] According to yet another embodiment, when the target pixel is a red or blue pixel and operates as a corner pixel, the adjacent pixel determiner 240 may determine not only pixels in the target core that are not PDAF pixels as adjacent pixels, but may also determine one or more green pixels that are adjacent to the target pixel and belong to another core as adjacent pixels. In addition, the adjacent pixel determiner 240 may perform conversion of pixel data of at least one green pixel that is adjacent to the target pixel and belongs to another core by using a local color gain. The local color gain may refer to a ratio between an average value of pixel data of at least one homogeneous pixel that is adjacent to the target pixel and an average value of at least one green pixel that is adjacent to the target pixel and belongs to another core.

[0039] In some embodiments, a red pixel may be a pixel that generates pixel data corresponding to the intensity of red light, a green pixel may be a pixel that generates pixel data corresponding to the intensity of green light, and a blue pixel may be a pixel that generates pixel data corresponding to the intensity of blue light. In some embodiments, red pixels, green pixels, and blue pixels may be determined based on a pattern arrangement. However, a PDAF pixel pair may include color filters of the same color. For example, a PDAF pixel included in a blue pixel group corresponding to blue and another PDAF pixel included in a green pixel group corresponding to green may constitute a PDAF pixel pair. In this case, a PDAF pixel included in a blue pixel group and another PDAF pixel included in a green pixel group may include color filters of the same color (e.g., green or white).

[0040] The defect detector 250 may compare the result of calculating the pixel data of the target pixel and the pixel data of the adjacent pixels with the threshold value, and may determine whether the target pixel is a defective pixel based on the result of the comparison. According to one embodiment, the defect detector 250 may determine whether the target pixel is a defective pixel based on the result of counting the number of times the absolute value of the value obtained by subtracting the pixel data of the adjacent pixels from the pixel data of the target pixel is less than the threshold value.

[0041] The defective pixel corrector 300 may perform defective pixel correction on a target pixel determined as a defective pixel by the defective pixel determiner 200. The defective pixel correction may be performed based on a mask having a predetermined size (e.g., 10×10), and may refer to an operation of calculating pixel data corresponding to a target pixel to be corrected for defective pixel correction after performing calculation (e.g., linear interpolation) of pixel data of at least one pixel that is a homogeneous (and / or heterogeneous) pixel relative to the target pixel within the mask, wherein the target pixel is located at the center of the mask.

[0042] Figure 2 is a flow chart illustrating a method of image signal processing, which method is preferably performed by executing program instructions by an image signal processor or some other programmable processor with suitable capabilities. Figure 3 is a schematic diagram showing an example of raw image data obtained from an image sensor, the raw image data being obtained by some embodiments based on the disclosed technology Figure 2 The image signal processing method operates (or manipulates) Figure 4 The present invention is a diagram showing some embodiments based on the disclosed technology. Figure 2 Schematic diagram of another example of an image signal processing method operating (or manipulating) raw image data.

[0043] Figure 5The present invention is a diagram showing some embodiments based on the disclosed technology. Figure 2 Schematic diagram of yet another example of raw image data operated (or manipulated) by the image signal processing method.

[0044] refer to Figure 2 , Figure 2 The image processing method shown can be Figure 1 The method may be performed by an image signal processor (ISP) 100. As described above, the method may also be performed by a properly programmed general-purpose digital signal processor (DSP) or a properly programmed general-purpose processor (eg, microprocessor, microcontroller).

[0045] When the target pixel is not a fixed defective pixel, the defective pixel determiner 200 may perform Figure 2 When the target pixel is a fixed defective pixel, the defective pixel determiner 200 may omit Figure 2 The image processing method can control the defective pixel corrector 300 to perform defective pixel correction on the target pixel.

[0046] According to one embodiment, the defective pixel determiner 200 may be executed by sequentially selecting target pixels according to a raster scanning method. Figure 2 Each of the image processing methods, but the scope of the disclosed technology is not limited thereto.

[0047] The image processing method can be divided into an operation for calculating the complexity of a target pixel (S100), an operation for calculating a threshold corresponding to the target pixel (S200), an operation for determining adjacent pixels of the target pixel (S300), and an operation for determining whether the target pixel is a defective pixel (S400).

[0048] In the disclosed technology, the image processing method for raw image data will be described using raw image data of a nine-Bayer pattern as an example. However, the pattern and size of the raw image data are not limited thereto, and the technical ideas described in the present disclosure can also be applied to any modified pattern and size as needed. The nine-Bayer pattern may refer to a pattern in which pixel groups arranged in a (3×3) matrix corresponding to the same color are arranged in a Bayer pattern. In addition, although for the convenience of description Figures 3 to 5 Each of the raw image data (IMG1 to IMG3) is shown to have a (5×5) size, but other embodiments are possible, and it should be noted that the raw image data corresponding to one frame may include pixel data arranged in a nine-Bayer pattern.

[0049] In addition, for convenience of description, a specific pixel (eg, G22) mentioned in the following description may indicate pixel data of a corresponding pixel or an actual pixel of an image sensing device.

[0050] First, the complexity calculator 220 may calculate the complexity of a target kernel including a target pixel on original image data ( S110 ).

[0051] exist Figure 3 , the first original image data IMG1 may include a first target core TK1 having green pixels (G22~G44) arranged in a (3×3) matrix. The first target core TK1 may include corner pixels (G22, G24, G42, G44, each of which is represented by a circle) respectively arranged at the vertices of the first target core TK1, a PDAF pixel (G32) that constitutes a pair of PDAF pixels together with a blue pixel (B31) located on the left side of the first target core TK1, and normal pixels (G23, G33, G34, G43, each of which is represented by a triangle). Here, a pair of PDAF pixels may refer to pixels that are horizontally (or vertically) adjacent to each other and share a microlens to obtain horizontal (or vertical) phase difference information. In addition, normal pixels may refer to pixels that are not corner pixels and are not used as PDAF pixels. Although Figures 3 to 5 An example case is shown in which a PDAF pixel is included in the target core as an example of a fixed defective pixel, but other embodiments are possible, and it should be noted that the image processing method described below can be applied not only to a case in which defective pixels other than the PDAF pixel are included in the target core, but also to another case in which the PDAF pixel and the defective pixel are included in the target core.

[0052] The complexity calculator 220 may calculate the complexity of the first target kernel TK1 using pixel data of pixels included in the first target kernel TK1. In some embodiments, the complexity calculator 220 may calculate the complexity CPX of the first target kernel TK1 through Equation 1 below.

[0053] [Formula 1]

[0054]

[0055] In Formula 1, 'n' may represent the number of valid (functional) pixels (Pv), which are pixels other than target pixels and fixed defect pixels (eg, PDAF pixels) in the first target kernel TK1. Figure 3As can be seen in the example, when the target pixel is set to G33, the valid pixel (Pv) can be the pixels (G22, G23, G24, G34, G42, G43, G44) among the pixels included in the first target core TK1 except the target pixel G33 and the PDAF pixel G32, where 'n' can be set to '7'.

[0056] The kernel average value (AVG) may correspond to an average value of pixel data of the valid pixels (Pv), 'Pv,i' may represent pixel data of each valid pixel (Pv), and 'i' may correspond to a serial number of each valid pixel (Pv).

[0057] According to Formula 1, the complexity can indicate how much the pixel data of each effective pixel is dispersed relative to the average value of the pixel data of the effective pixels included in the target core. Since it is impossible to identify whether the target pixel is a defective pixel during the complexity calculation, the target pixel can be excluded from the complexity calculation. In addition, since the PDAF pixel is a pixel that can obtain phase difference information, the PDAF pixel cannot generate ordinary color image data, so the PDAF pixel can be excluded from the complexity calculation.

[0058] exist Figure 4 , the second original image data IMG2 may include a second target core TK2 having blue pixels (B22-B44) arranged in a (3×3) matrix. The second target core TK2 may include corner pixels (B22, B24, B42, B44, each represented by a circle) respectively disposed at the vertices of the second target core TK2, a PDAF pixel (B32) constituting a pair of PDAF pixels together with a green pixel (G31) disposed on the left side of the second target core TK2, and normal pixels (B23, B33, B34, B43, each represented by a triangle).

[0059] The complexity calculator 220 may calculate the complexity of the second target kernel TK2 using pixel data of pixels included in the second target kernel TK2. In some embodiments, the complexity calculator 220 may calculate the complexity of the second target kernel TK2 in the same manner as the complexity of the first target kernel TK1 through the above formula 1.

[0060] exist Figure 5, the third original image data IMG3 may include a third target core TK3 having red pixels (R22 to R44) arranged in a (3×3) matrix. The third target core TK3 may include corner pixels (R22, R24, R42, R44, each of which is represented by a circle) respectively disposed at the vertices of the third target core TK3, a PDAF pixel (R32) constituting a pair of PDAF pixels together with a green pixel (G31) disposed on the left side of the third target core TK3, and normal pixels (R23, R33, R34, R43, each of which is represented by a triangle).

[0061] The complexity calculator 220 may calculate the complexity of the third target kernel TK3 using pixel data of pixels included in the third target kernel TK3. In some embodiments, the complexity calculator 220 may calculate the complexity of the third target kernel TK3 in the same manner as the complexity of the first target kernel TK1 through the above formula 1.

[0062] The threshold calculator 230 may determine whether the target pixel is a corner pixel ( S210 ).

[0063] When the target pixel is a corner pixel (Yes in S210 ), the threshold calculator 230 may calculate the contrast of pixels of different colors adjacent to the target pixel ( S220 ).

[0064] Will refer to Figure 3 An example case where the target pixel is the corner pixel G22 is described. The threshold calculator 230 may select the maximum pixel data (Rmax) and the minimum pixel data (Rmin) from the pixel data of the heterochromatic pixels (R12, R13, R14) adjacent to the corner pixel G22 serving as the target pixel, and may calculate a value (Rmax-Rmin) obtained by subtracting the minimum pixel data (Rmin) from the maximum pixel data (Rmax) as the contrast of the heterochromatic pixels adjacent to the target pixel G22. Figure 3 In the example of , since the PDAF pixel (B31) is included in the blue pixels (B21, B31, B41) disposed on the left side of the corner pixel G22, the red pixels (R12, R13, R14) disposed on the upper side of the corner pixel G22 are selected as the different-color pixels, but other different-color pixels located in any direction from the corner pixel can also be used for contrast calculation. For example, when the target pixel is the corner pixel G44, the blue pixels (B25, B35, B45) disposed on the right side of the corner pixel G44 or the red pixels (R52, R53, R54) disposed below the corner pixel G44 can be optionally used for contrast calculation, or the average value of the contrast calculated using the blue pixels (B25, B35, B45) and the contrast calculated using the red pixels (R52, R53, R54) can also be calculated as the contrast of the different-color pixels.

[0065] Although it has Figure 3 The corner pixels (G22, G24, G42, G44) are described as examples, but are not the same as Figure 4 The contrast ratio of the adjacent heterochromatic pixels of the corner pixels (B22, B24, B42, B44) is Figure 5 The contrast ratios of opposite-color pixels adjacent to the corner pixels (R22, R24, R42, R44) can also be calculated in substantially the same manner.

[0066] The threshold calculator 230 may calculate a threshold of the target pixel using the complexity of the target kernel and the contrast of heterochromatic pixels adjacent to the target pixel ( S230 ).

[0067] In some embodiments, the threshold calculator 230 may use the following Equation 2 to calculate the threshold THD of the target pixel.

[0068] [Formula 2]

[0069] THD=a*CPX+b*CTR

[0070] In Formula 2, CTR may refer to the contrast of heterochromatic pixels adjacent to the target pixel. 'a' is a first weight and may refer to the weight of the complexity of the target kernel (CPX) relative to the threshold value (THD), for example, 'a' may be 1 to 1.5. 'b' is a second weight and may refer to the weight of the contrast of heterochromatic pixels adjacent to the target pixel (CTR) relative to the threshold value (THD), for example, 'b' may be 0.5 to 1. 'a' and 'b' may be experimentally predetermined values ​​in consideration of the performance of detecting defective pixels.

[0071] When the target pixel is a normal pixel rather than a corner pixel (No in S210 ), the threshold calculator 230 may calculate a threshold of the target pixel using the complexity of the target kernel ( S240 ).

[0072] In some embodiments, the threshold calculator 230 may use the following Equation 3 to calculate the threshold THD of the target pixel.

[0073] [Formula 3]

[0074] THD=a*CPX

[0075] Here, unlike Formula 2, the threshold THD may be calculated using only the complexity CPX of the target kernel. 'a' is a first weight and may represent a weight of the complexity (CPX) of the target kernel relative to the threshold (THD), for example, 'a' may be 1 to 1.5. 'a' may be a value experimentally predetermined in consideration of the performance of detecting defective pixels.

[0076] When the target pixel is a green pixel (Yes in S305 ) after calculation of the threshold is completed ( S230 ), the neighboring pixel determiner 240 may determine valid pixels in the target core and green pixels diagonally adjacent to the target pixel as neighboring pixels ( S310 ).

[0077] exist Figure 3 , when the target pixel is the green pixel G22, the adjacent pixel determiner 240 may determine the valid pixels (G23, G24, G33, G34, G42, G43, G44) in the first target kernel TK1 and the green pixel G11 diagonally adjacent to the target pixel G22 as adjacent pixels.

[0078] Although an example case where the target pixel is the green pixel G22 has been described for convenience of description, even when the target pixel is set to any one of the other green pixels (G24, G42, G44), the adjacent pixels can be determined in the same manner.

[0079] When the target pixel is a blue or red pixel after completing the calculation of the threshold ( S230 ) ( S305 : No), the adjacent pixel determiner 240 may calculate a local color gain between a green pixel adjacent to the target pixel and a homogeneous pixel adjacent to the target pixel ( S320 ).

[0080] exist Figure 4 , when the target pixel is a blue pixel B22, the adjacent pixel determiner 240 may calculate the average value of the green pixels (G12, G13, G14) adjacent to the target pixel B22, may calculate the average value of the homogeneous pixels (B23, B24) adjacent to the target pixel B22, and may calculate the local color gain by dividing the average value of the homogeneous pixels (B23, B24) by the average value of the green pixels (G12, G13, G12, G13).

[0081] As from Figure 4As can be seen from the example, since the PDAF pixel G31 is included in the green pixels (G21, G31, G41) set on the left side of the blue pixel B22, the local color gain can be calculated by selecting the green pixels (G12, G13, G14) set on the upper side of the blue pixel B22. However, the local color gain can be calculated using green pixels set in any direction from the target pixel. For example, in the case of the target pixel B44, the green pixels (G25, G35, G45) set on the right side of the target pixel B44 or the green pixels (G52, G53, G54) set on the lower side of the target pixel B44 can be optionally used to calculate the local color gain. In this case, when the green pixels (G25, G35, G45) are used, the blue pixels (B24, B34) adjacent to the green pixels (G25, G35, G45) can be used to calculate the local color gain. Alternatively, when the green pixels (G52, G53, G54) are used, the blue pixels (B42, B43) adjacent to the green pixels (G52, G53, G54) may be used to calculate the local color gain.

[0082] exist Figure 5 , when the target pixel is a red pixel R22, the adjacent pixel determiner 240 may calculate the average value of the green pixels (G12, G13, G14) adjacent to the target pixel R22, may calculate the average value of the homogeneous pixels (R23, R24) adjacent to the target pixel R22, and may calculate the local color gain by dividing the average value of the homogeneous pixels (R23, R24) by the average value of the green pixels (G12, G13, G14).

[0083] As from Figure 5As can be seen from the example, since the PDAF pixel G31 is included in the green pixels (G21, G31, G41) set on the left side of the red pixel R22, the local color gain can be calculated by selecting the green pixels (G12, G13, G14) set on the upper side of the red pixel R22. However, the local color gain can be calculated using green pixels set in any direction from the target pixel. For example, in the case of the target pixel R44, the green pixels (G25, G35, G45) set on the right side of the target pixel R44 or the green pixels (G52, G53, G54) R44 set on the lower side of the target pixel R44 can be optionally used to calculate the local color gain. In this case, when the green pixels (G25, G35, G45) are used, the red pixels (R24, R34) adjacent to the green pixels (G25, G35, G45) can be used to calculate the local color gain. Alternatively, when the green pixels (G52, G53, G54) are used, the red pixels (R42, R43) adjacent to the green pixels (G52, G53, G54) may be used to calculate the local color gain.

[0084] The adjacent pixel determiner 240 may convert each green pixel adjacent to the target pixel into a homogeneous pixel of the target pixel using a local color gain (S330). Since the local color gain refers to the average ratio between the green pixels adjacent to the target pixel and the valid pixels adjacent to the green pixels in the target core, the adjacent pixel determiner 240 may convert each green pixel into a homogeneous pixel of the target pixel by multiplying the local color gain by each green pixel.

[0085] exist Figure 4 , the adjacent pixel determiner 240 can multiply the pixel data of each green pixel (G12, G13, G14) by the local color gain between the green pixel (G12, G13, G14) and the blue pixel (B23, B24), and can calculate the pixel data of each green pixel (G12', G13', G14') converted into a blue pixel of the homogeneous pixel used as the target pixel B22.

[0086] exist Figure 5 , the adjacent pixel determiner 240 can multiply the pixel data of each green pixel (G12, G13, G14) by the local color gain between the green pixel (G12, G13, G14) and the red pixel (R23, R24), and can calculate the pixel data of each green pixel (G12", G13", G14") that is converted into a red pixel of the same quality as the target pixel R22.

[0087] After performing operation S330 , the neighboring pixel determiner 240 may determine valid pixels in the target core and green pixels converted into homogeneous pixels of the target pixel as neighboring pixels ( S340 ).

[0088] exist Figure 4 In the case of the target pixel B22, the adjacent pixel determiner 240 can determine the valid pixels (B23, B24, B33, B34, B42, B43, B44) in the second target kernel TK2 and the green pixels (G12', G13', G14') converted into blue pixels used as homogeneous pixels of the target pixel B22 and adjacent to the target pixel B22.

[0089] exist Figure 5 In the case of the target pixel R22, the adjacent pixel determiner 240 can determine the valid pixels (R23, R24, R33, R34, R42, R43, R44) in the third target kernel TK3 and the green pixels (G12", G13", G14") that are converted into red pixels used as homogeneous pixels of the target pixel R22 and are adjacent to the target pixel R22.

[0090] When the target pixel is a normal pixel, the threshold calculation is completed through step S240, and through step S240, the adjacent pixel determiner 240 can determine the valid pixels within the target kernel as adjacent pixels (S350).

[0091] For example, in Figure 3 In the case of the target pixel G23 shown, the neighboring pixel determiner 240 may determine the valid pixels (G22, G24, G33, G34, G42, G43, G44) in the first target kernel TK1 as the neighboring pixels.

[0092] That is, when the target pixel is not a corner pixel, the adjacent pixel determiner 240 may determine the valid pixels in the target core as pixels adjacent to the target pixel, but when the target pixel is not a corner pixel, the adjacent pixel determiner 240 may determine the valid pixels in the target core and at least one reference pixel (for example, a green pixel located in a diagonal direction relative to the target pixel, or a green pixel converted into a homogeneous pixel of the target pixel) as adjacent pixels.

[0093] The defect detector 250 may compare a difference between each of the determined neighboring pixels and the target pixel with a threshold value of the target pixel, and may determine whether the target pixel is a defective pixel ( S410 ).

[0094] exist Figure 3, the neighboring pixels of the target pixel G22 (which is a corner pixel and a green pixel) may be the valid pixels (G23, G24, G33, G34, G42, G43, G44) in the first target kernel TK1 and the green pixel G11 diagonally adjacent to the target pixel G22. Hereinafter, for the convenience of description, the pixel G22 among the pixels (G22, G24, G42, G44) which are corner pixels and green pixels will be described as an example, but substantially the same operation may also be performed on the remaining pixels (G24, G42, G44).

[0095] The defect detector 250 may compare the difference between the target pixel G22 and each adjacent pixel (G11, G23, G24, G33, G34, G42, G43, G44) and the threshold, and may determine the determination value by counting the number of times the difference is less than the threshold. That is, when the difference between the target pixel G22 and the adjacent pixel G11 is less than the threshold, the defect detector 250 may increase the determination value by one ('1'). When the difference between the target pixel G22 and the adjacent pixel G11 is equal to or greater than the threshold, the defect detector 250 may maintain the determination value. In addition, the defect detector 250 may compare the difference between the target pixel G22 and each adjacent pixel (G23, G24, G33, G34, G42, G43, G44) and the threshold, and may determine the determination value based on the result of the comparison.

[0096] When the determination value is set to zero ('0'), the defect detector 250 may determine that the target pixel G22 is a defective pixel. When the determination value is set to zero ('0'), the difference between the target pixel G11 and each of the neighboring pixels (G11, G23, G24, G33, G34, G42, G43, G44) is higher than a threshold value based on complexity (complexity is an average indicator indicating how much the pixel data of each pixel is dispersed relative to the average value of the pixel data of the pixels included in the target core), making it highly likely that the target pixel G22 in which the difference between the target pixel G22 and each of the neighboring pixels (G11, G23, G24, G33, G34, G42, G43, G44) deviates from the general range is a defective pixel.

[0097] When the determination value is equal to or greater than '1', the defect detector 250 can determine that the target pixel G22 is not a defective pixel (i.e., the target pixel G22 is a normal pixel). When the determination value is set to 1 or greater, it can be understood that there is a case where the difference between the target pixel G22 and each adjacent pixel (G11, G23, G24, G33, G34, G42, G43, G44) is less than the threshold value based on complexity. Therefore, it is very likely that the target pixel G22 in which the difference between the target pixel G22 and each adjacent pixel (G11, G23, G24, G33, G34, G42, G43, G44) is within the normal range is not a defective pixel.

[0098] Assume that the first texture TX1 exists in Figure 3 In the first original image data (IMG1) shown, only the corner pixel G22 in the first target kernel TK1 may be included in the first texture TX1. The first texture TX1 may correspond to a specific object in the scene, and the pixels (G11, R12, B21, G22) included in the first texture TX1 may have similar pixel data. On the other hand, pixels not included in the first texture TX1 (e.g., G23, G24, G33, G34, G42, G43, G44, etc.) may have pixel data significantly different from the pixel data of the pixels (G11, R12, B21, G22) included in the first texture TX1. If only the pixels (e.g., G23, G24, G33, G34, G42, G43, G44) included in the first target kernel TK1 and not included in the first texture TX1 are used to determine whether the corner pixel G22 included in the first texture TX1 is a defective pixel, the corner pixel G22 may be erroneously determined as a defective pixel.

[0099] However, in the case of the corner pixel G22 as shown in this embodiment, the green pixel G11 included in the first texture TX1 but not included in the first target kernel TK1 may be included in the adjacent pixels, so that the corner pixel G22 can be prevented from being erroneously determined as a defective pixel.

[0100] In addition, in the case of the corner pixel G22, not only the complexity is reflected in the threshold value, but also the contrast corresponding to the difference between the pixel R12 included in the first texture TX1 and each pixel (R13 or R14) not included in the first texture TX1 is also reflected in the threshold value, so that the sensitivity of determining whether the corner pixel G22 is a defective pixel can be adjusted (for example, can be adjusted to a low sensitivity). That is, when the threshold value is increased by reflecting the contrast in the threshold value, the difference between the corner pixel G22 included in the first texture TX1 and the valid pixel in the first target kernel TK1 not included in the first texture TX1 can be less than the threshold value, so that the possibility of erroneously determining the corner pixel G22, which is a normal pixel, as a defective pixel can be reduced.

[0101] exist Figure 4 In the embodiment, the neighboring pixels of the target pixel B22 (which is a corner pixel and a blue pixel) may be valid pixels (B23, B24, B33, B34, B42, B43, B44) in the second target kernel TK2, and may also be green pixels (G12', G13', G14') that are converted into blue pixels corresponding to homogeneous pixels of the target pixel B22 and are adjacent to the target pixel B22. Hereinafter, the pixel B22 among the pixels (B22, B24, B42, B44) that are corner pixels and blue pixels will be described as an example, but substantially the same operation may also be performed on the remaining pixels (B24, B42, B44). In addition, the pixel B22 that is a corner pixel and a blue pixel may be converted into a green pixel (G12', G13', G14'). Figure 5 The target pixels (R22, R24, R42, R44) of the red pixels and corner pixels perform substantially the same operation, so redundant descriptions thereof will be omitted here for the sake of brevity.

[0102] The defect detector 250 may compare the difference between the target pixel B22 and each adjacent pixel (G12', G13', G14', B23, B24, B33, B34, B42, B43, B44) and the threshold, and may determine the determination value by counting the number of times the difference is less than the threshold. That is, when the difference between the target pixel B22 and the adjacent pixel (G12') is less than the threshold, the defect detector 250 may increase the determination value by one ('1'). When the difference between the target pixel B22 and the adjacent pixel (G12') is equal to or greater than the threshold, the defect detector 250 may maintain the determination value. In addition, the defect detector 250 may compare the difference between the target pixel B22 and each adjacent pixel (G13', G14', B23, B24, B33, B34, B42, B43, B44) and the threshold, and may determine the determination value based on the result of the comparison.

[0103] When the determination value is set to zero ('0'), the defect detector 250 can determine that the target pixel B22 is a defective pixel. When the determination value is set to zero ('0'), this means that the difference between the target pixel B22 and each of the neighboring pixels (G12', G13', G14', B23, B24, B33, B34, B42, B43, B44) is higher than a threshold value based on complexity (complexity is an average indicator indicating how much the pixel data of each pixel is dispersed relative to the average value of the pixel data of the pixels included in the target core), making it highly likely that the target pixel B22 in which the difference between the target pixel B22 and each of the neighboring pixels (G12', G13', G14', B23, B24, B33, B34, B42, B43, B44) deviates from the general range is a defective pixel.

[0104] When the determination value is equal to or greater than '1', the defect detector 250 can determine that the target pixel B22 is not a defective pixel. When the determination value is set to 1 or greater, it can be understood that there is a case where the difference between the target pixel G22 and each adjacent pixel (G12', G13', G14', B23, B24, B33, B34, B42, B43, B44) is less than the threshold value based on complexity. Therefore, it is very likely that the target pixel B22 in which the difference between the target pixel B22 and each adjacent pixel (G12', G13', G14', B23, B24, B33, B34, B42, B43, B44) is within the normal range is not a defective pixel.

[0105] Assume that the second texture TX2 exists in Figure 4 In the second original image data (IMG2) shown, only the corner pixel B22 in the second target kernel TK2 can be included in the second texture TX2. The second texture TX2 can correspond to a specific object on the scene, and the pixels (R11, G12, G21, B22) included in the second texture TX2 can have similar pixel data. On the other hand, pixels not included in the second texture TX2 (e.g., B23, B24, B33, B34, B42, B43, B44, etc.) can have pixel data significantly different from the pixel data of the pixels (R11, G12, G21, B22) included in the second texture TX2. If only the pixels (e.g., B23, B24, B33, B34, B42, B43, B44) included in the second target kernel TK2 but not included in the second texture TX2 are used to determine whether the corner pixel B22 included in the second texture TX2 is a defective pixel, the corner pixel B22 may be erroneously determined as a defective pixel.

[0106] However, in the case of the corner pixel B22 as shown in the present embodiment, the pixel (G12') included in the second texture TX2 but not included in the second target kernel TK2 is included in the adjacent pixels, so that the corner pixel B22 can be prevented from being erroneously determined as a defective pixel. In this case, the pixel data of the green pixel G12 is not changed and is not used, while the green pixel (G12') converted by the local color gain can be used, thereby improving the accuracy of determining the defective pixel.

[0107] In addition, in the case of the corner pixel B22, not only the complexity is reflected in the threshold value, but also the contrast corresponding to the difference between the pixel (G12') included in the second texture TX2 and each pixel (G13' or G14') not included in the second texture TX2 is also reflected in the threshold value, so that the sensitivity of determining whether the corner pixel B22 is a defective pixel can be adjusted (for example, can be adjusted to a low sensitivity). That is, when the threshold value is increased by reflecting the contrast in the threshold value, the difference between the corner pixel B22 included in the second texture TX2 and the valid pixel in the second target kernel TK2 not included in the second texture TX2 can be less than the threshold value, so that the possibility of erroneously determining the corner pixel B22, which is a normal pixel, as a defective pixel can be reduced.

[0108] exist Figure 3 In the example, the neighboring pixels of the target pixel G23 (which is a normal pixel not used as a corner pixel) may be valid pixels (G22, G24, G33, G34, G42, G43, G44) in the first target kernel TK1. Hereinafter, the pixel G23 among the normal pixels (G23, G33, G42, G44) will be described as an example, but substantially the same operation may be performed on the remaining pixels (G33, G42, G44). Figure 4 or Figure 5 The target pixels (B23, B33, B34, B43, R23, R33, R34, R43) corresponding to the ordinary pixels perform basically the same operations, so the redundant description thereof will be omitted here for the sake of brevity.

[0109] The defect detector 250 may compare the difference between the target pixel G23 and each adjacent pixel (G22, G24, G33, G34, G42, G43, G44) and the threshold, and may determine the determination value by counting the number of times the difference is less than the threshold. That is, when the difference between the target pixel G23 and the adjacent pixel G22 is less than the threshold, the defect detector 250 may increase the determination value by one ('1'). When the difference between the target pixel G23 and the adjacent pixel G22 is equal to or greater than the threshold, the defect detector 250 may maintain the determination value. In addition, the defect detector 250 may compare the difference between the target pixel G23 and each adjacent pixel (G24, G33, G34, G42, G43, G44) and the threshold, and may determine the determination value based on the result of the comparison.

[0110] When the determination value is set to zero ('0'), the defect detector 250 can determine that the target pixel G23 is a defective pixel. When the determination value is set to zero ('0'), this means that the difference between the target pixel G23 and each of the neighboring pixels (G22, G24, G33, G34, G42, G43, G44) is higher than a threshold value based on complexity (complexity is an average indicator indicating how much the pixel data of each pixel is dispersed relative to the average value of the pixel data of the pixels included in the target core), making it highly likely that the target pixel G23 in which the difference between the target pixel G23 and each of the neighboring pixels (G22, G24, G33, G34, G42, G43, G44) deviates from the general range is a defective pixel.

[0111] When the determination value is equal to or greater than '1', the defect detector 250 can determine that the target pixel G23 is not a defective pixel. When the determination value is set to 1 or greater, it can be understood that there is a case where the difference between the target pixel G23 and each adjacent pixel (G22, G24, G33, G34, G42, G43, G44) is less than the threshold value based on complexity. Therefore, it is very likely that the target pixel G23 in which the difference between the target pixel G23 and each adjacent pixel (G22, G24, G33, G34, G42, G43, G44) is within the normal range is not a defective pixel.

[0112] In some embodiments, the accuracy of determining whether a corner pixel in a target core that is uniquely included in a specific texture is a defective pixel can be improved. To this end, although the embodiment of the disclosed technology is described as using a method for extending the range of adjacent pixels and a method for reflecting contrast in a threshold together for the sake of convenience, the scope of the disclosed technology is not limited thereto, and only one of the two methods may be used as needed.

[0113] In the disclosed technology, color pixels of the same color arranged in a (3×3) matrix having three rows and three columns can form one core, and a nine-Bayer pattern in which a plurality of cores form a Bayer pattern has been described as an example, but the scope of the disclosed technology is not limited thereto. That is, the technical idea of ​​the disclosed technology can be applied to any pattern in which color pixels of the same color arranged in a (N×N) matrix having N rows and N columns (where N is an integer of 3 or more) constitute a single core.

[0114] As is apparent from the above description, the image signal processor and the image signal processing method based on some embodiments of the disclosed technology can improve the accuracy of the operation of determining whether a corner pixel in a target kernel is a defective pixel.

[0115] Embodiments of the disclosed technology can provide various effects that can be directly or indirectly recognized through the above-mentioned patent documents.

[0116] Although a number of illustrative embodiments have been described, it should be understood that modifications and enhancements to the disclosed embodiments and other embodiments may be devised based on what is described and / or illustrated in this patent document.

[0117] Figure 6 is shown with Figure 1 A block diagram of an example of a computing device 1000 corresponding to an image signal processor.

[0118] Reference Figure 6 , the computing device 1000 may represent a computer program product for executing Figure 1 An embodiment of a hardware configuration of the operation of the image signal processor 100.

[0119] The computing device 1000 may be mounted on a chip separate from a chip on which the image sensing device is mounted. According to one embodiment, the chip on which the image sensing device is mounted and the chip on which the computing device 1000 is mounted may be implemented in one package, for example, a multi-chip package (MCP), but the scope of the present invention is not limited thereto.

[0120] in addition, Figure 1 The internal configuration or arrangement of the image sensing device and the image signal processor 100 described in the embodiment may vary according to the embodiment. For example, at least a portion of the image sensing device may be included in the image signal processor 100. Alternatively, at least a portion of the image signal processor 100 may be included in the image sensing device. In this case, at least a portion of the image signal processor 100 may be mounted together on a chip on which the image sensing device is mounted.

[0121] The computing device 1000 may include a processor 1010 , a memory 1020 , an input / output interface 1030 , and a communication interface 1040 .

[0122] Processor 1010 can process execution Figure 1 Data and / or instructions required for the operation of components 200 to 300 of the image signal processor 100 described in the specification.

[0123] The memory 1020 may store data and / or instructions required to perform operations of the components 200 to 300 of the image signal processor 100, and may be accessed by the processor 1010. For example, the memory 1020 may be a volatile memory (e.g., a dynamic random access memory (DRAM), a static random access memory (SRAM), etc.) or a nonvolatile memory (e.g., a programmable read-only memory (PROM), an erasable PROM (EPROM), etc., an EEPROM (electrically erasable PROM), a flash memory, etc.).

[0124] That is, a computer program for executing the operation of the image signal processor 100 disclosed herein is recorded in the memory 1020 and is executed and processed by the processor 1010 , thereby implementing the operation of the image signal processor 100 .

[0125] The input / output interface 1030 is an interface that connects an external input device (eg, keyboard, mouse, touch pad, etc.) and / or an external output device (eg, display) to the processor 1010 to allow data transmission and reception.

[0126] The communication interface 1040 is a component capable of transmitting and receiving various data with an external device (eg, an application processor, an external memory, etc.), and may be a means supporting wired or wireless communication.

Claims

1. An image signal processor, comprising: a threshold calculator that calculates a threshold of a target pixel based on the complexity of a target kernel including the target pixel; an adjacent pixel determiner, which: when the target pixel is a corner pixel, determines one or more valid pixels of the target core and at least one reference pixel located outside the target core as adjacent pixels of the target pixel; and a defect detector that determines whether the target pixel is a defective pixel based on a result of comparing a difference between pixel data of the adjacent pixels and pixel data of the target pixel with the threshold value, The corner pixels are pixels located at the vertices of the target core.

2. The image signal processor according to claim 1, wherein: The complexity is the average deviation of valid pixels included in the target kernel.

3. The image signal processor according to claim 1, wherein: The valid pixels include pixels in the target core other than the target pixel and a fixed defective pixel serving as a predetermined defective pixel.

4. The image signal processor according to claim 3, wherein: The fixed defective pixels include PDAF pixels, where PDAF refers to phase difference detection auto focus.

5. The image signal processor according to claim 1, wherein: When the target pixel is the corner pixel, the threshold calculator calculates a contrast of a different-color pixel, which is adjacent to the target pixel and corresponds to a color different from that of the target pixel, and wherein the threshold calculator calculates the threshold by calculating the complexity and the contrast.

6. The image signal processor according to claim 5, wherein: The contrast is a value obtained by subtracting the lowest pixel data from the highest pixel data among the pixel data of the different-color pixels.

7. The image signal processor according to claim 1, wherein: When the target pixel is both the corner pixel and a green pixel, the at least one reference pixel includes a green pixel located outside the target pixel.

8. The image signal processor according to claim 1, wherein: When the target pixel is at least one of a red pixel located at a corner and a blue pixel located at a corner, the adjacent pixel determiner calculates a local color gain as a ratio between an average value of pixel data of valid pixels adjacent to the target pixel in the target core and an average value of pixel data of green pixels adjacent to the target core.

9. The image signal processor according to claim 8, wherein: The adjacent pixel determiner multiplies pixel data of green pixels adjacent to the target core by the local color gain, thereby converting the green pixels into homogeneous pixels of the target core.

10. The image signal processor according to claim 9, wherein: The at least one reference pixel comprises a converted green pixel.

11. The image signal processor according to claim 1, wherein: When the target pixel is a normal pixel other than the corner pixel, the neighboring pixel determiner determines a valid pixel of the target kernel as the neighboring pixel.

12. The image signal processor according to claim 1, wherein: The defect detector determines a determination value by counting the number of times the difference is smaller than the threshold value.

13. The image signal processor according to claim 12, wherein: The defect detector: When the determination value is set to zero "0", the target pixel is determined to be the defective pixel; as well as When the determination value is set to 1 or more, the target pixel is determined to be a normal pixel instead of the defective pixel.

14. The image signal processor according to claim 1, wherein: The target kernel includes pixels corresponding to the same color as that of the target pixel.

15. The image signal processor according to claim 1, wherein: The target core comprises: pixels arranged in an N×N matrix comprising N rows and N columns, Here, N is an integer of 3 or greater.

16. An image signal processor, comprising: a complexity calculator that calculates the complexity of a target kernel including a target pixel; a threshold calculator, which: when the target pixel is a corner pixel, calculates a contrast ratio of a pixel of a different color, and calculates a threshold value of the target pixel based on the complexity and the contrast ratio, the pixel of a different color being adjacent to the target pixel and corresponding to a color different from that of the target pixel; and a defect detector that determines whether the target pixel is a defective pixel based on a result of comparing a difference between pixel data of pixels adjacent to the target pixel and pixel data of the target pixel with the threshold value, The corner pixels are pixels located at the vertices of the target kernel.

17. The image signal processor according to claim 16, wherein: The contrast is a value obtained by subtracting the lowest pixel data from the highest pixel data among the pixel data of the different-color pixels.

18. The image signal processor according to claim 16, further comprising: An adjacent pixel determiner is configured to determine, when the target pixel is a corner pixel, valid pixels of the target core and at least one reference pixel outside the target core as adjacent pixels of the target pixel.

19. The image signal processor according to claim 18, wherein: When the target pixel is a red pixel or a blue pixel and is used as the corner pixel, the adjacent pixel determiner calculates a local color gain, which is a ratio between an average value of pixel data of valid pixels adjacent to the target pixel in the target core and an average value of pixel data of green pixels adjacent to the target core, and multiplies pixel data of green pixels adjacent to the target core by the local color gain to convert the green pixels into homogeneous pixels of the target core; and The at least one reference pixel comprises a converted green pixel.

20. An image signal processor, comprising: a complexity calculator that calculates the complexity of a target kernel including a target pixel; a threshold calculator, which: determines whether the target pixel is a corner pixel located at a vertex of the target kernel, and calculates a threshold of the target pixel based on the complexity; an adjacent pixel determiner, which determines that a valid pixel of the target kernel is an adjacent pixel when the target pixel is not the corner pixel; and A defect detector determines whether the target pixel is a defective pixel according to a result of comparing a difference between the pixel data of the adjacent pixels and the pixel data of the target pixel with the threshold value.

Images