Image processing device, image sensor module, image processing method and device

Abstract

The embodiment of the present invention provides an image processing device, an image sensor module, an image processing method and an apparatus. By obtaining a sample image to be processed including bad pixels from an original image to reduce the amount of pixels, it is convenient to determine the bad pixel truncation contour including the bad pixel from the sample image to be processed. Then, a first pixel point is selected from the bad pixel truncation contour to fit the bad pixel truncation contour so that the bad pixel truncation contour is continuous, and then the fitted bad pixel truncation contour can divide the sample image to be processed into multiple sample image units to be processed. Finally, the second pixel point selected from the multiple sample image units to be processed is used to fill the bad pixel, so as to obtain a target image with complete pixels. In this way, by filling the bad pixel, an image with complete pixels can be obtained even if there are bad pixels in the image sensor.

Description

Technical Field

[0001] The present invention relates to the field of image processing technology, and in particular to image processing equipment, image sensor modules, image processing methods and devices. Background Art

[0002] In the camera module manufacturing industry, if there are defects in the image sensor manufacturing process or errors in the conversion of light signals, some pixels in the image will have incorrect information, resulting in inaccurate pixel values ​​in the image. These defective pixels are called bad pixels.

[0003] For some low-cost, consumer image sensors, there will be a lot of bad pixels. In addition, the number of bad pixels of image sensors will increase over time and in high temperature environments, thus destroying the clarity and integrity of the image. Therefore, there is an urgent need for a technology that can partially or completely eliminate the imaging defects caused by bad pixels. Summary of the invention

[0004] The embodiments of the present invention solve the technical problem of incomplete imaging when there are bad pixels in the image sensor by providing an image processing device, an image sensor module, an image processing method and an apparatus.

[0005] In a first aspect, the present invention provides an image processing method through an embodiment of the present invention, comprising: obtaining a sample image to be processed including bad pixels from an original image; determining a bad pixel truncation contour including the bad pixel from the sample image to be processed; selecting a first pixel point from the bad pixel truncation contour to fit the bad pixel truncation contour, wherein the fitted bad pixel truncation contour divides the sample image to be processed into a plurality of sample image units to be processed; and filling the bad pixel points with a second pixel point selected from the plurality of sample image units to be processed to obtain a target image with complete pixels.

[0006] Preferably, the step of obtaining a sample image to be processed including bad pixels from an original image comprises: performing imaging based on an image sensor having bad pixels to obtain the original image; and obtaining a sample image to be processed including the bad pixels from the original image based on coordinate information of the bad pixels.

[0007] Preferably, based on the coordinate information of the bad pixel, a sample image to be processed including the bad pixel is obtained from the original image, including: using the coordinate information of the bad pixel to locate the bad pixel on the original image; based on a preset sampling size, intercepting the bad pixel and its surrounding pixels to obtain the sample image to be processed.

[0008] Preferably, the selecting a first pixel point from the bad spot truncation contour to fit the bad spot truncation contour includes: determining the number of pixel points required to fit the bad spot truncation contour based on the size of the pixel bad spot, and selecting a first pixel point from a contour segment of the bad spot truncation contour that does not include the pixel bad spot based on the number of pixel points; and using the first pixel point to fit a contour segment of the bad spot truncation contour that includes the pixel bad spot.

[0009] Preferably, the method of filling the bad pixel with a second pixel point selected from the multiple sample image units to be processed includes: determining a bad pixel compensation area centered on the bad pixel point from the sample image units to be processed; selecting the second pixel point in the bad pixel compensation area, and filling the bad pixel point with the second pixel point.

[0010] Preferably, the selecting the second pixel point in the bad pixel compensation area and using the second pixel point to fill the bad pixel point includes: determining a reference texture of the area where the bad pixel point is located; based on the reference texture, selecting the second pixel point from the bad pixel compensation area; performing difference processing on the second pixel point to obtain a compensation pixel, and using the compensation pixel to fill the bad pixel point.

[0011] In a second aspect, the present invention provides an image processing device through an embodiment of the present invention, including:

[0012] An image bad pixel locating unit, used for obtaining a sample image to be processed including bad pixels from an original image;

[0013] A bad pixel contour determining unit, used for determining a bad pixel truncation contour including the bad pixel from the sample image to be processed;

[0014] A first pixel filling unit is used to select a first pixel point from the bad pixel truncation contour to fit the bad pixel truncation contour, wherein the fitted bad pixel truncation contour divides the sample image to be processed into a plurality of sample image units to be processed;

[0015] The second pixel filling unit is used to fill the pixel bad points with the second pixel points selected from the plurality of sample image units to be processed, so as to obtain a target image with complete pixels.

[0016] In a third aspect, the present invention provides an image processing device through an embodiment of the present invention, comprising a memory, a processor, and an image algorithm stored in the memory and executable on the processor, wherein the processor implements any one of the implementations of the first aspect when executing the image algorithm.

[0017] In a fourth aspect, the present invention provides an image sensor module through an embodiment of the present invention, comprising the image processing device described in the third aspect, and an image sensor electrically connected to the image processing device.

[0018] In a fifth aspect, the present invention provides a computer-readable storage medium through an embodiment of the present invention, on which an image algorithm is stored, and when the image algorithm is executed by a processor, any implementation method in the first aspect is implemented.

[0019] One or more technical solutions provided in the embodiments of the present invention have at least the following technical effects or advantages:

[0020] First, a sample image to be processed including bad pixels is obtained from the original image to reduce the number of pixels, so as to facilitate the determination of the bad pixel truncation contour including the bad pixel from the sample image to be processed. Then, a first pixel point is selected from the bad pixel truncation contour to fit the bad pixel truncation contour so that the bad pixel truncation contour is continuous, and then the bad pixel truncation contour after fitting can divide the sample image to be processed into multiple sample image units to be processed. Finally, the bad pixel is filled with the second pixel point selected from the multiple sample image units to be processed, so as to obtain a target image with complete pixels. In this way, by filling the bad pixel, an image with complete pixels can be obtained even if there are bad pixels in the image sensor. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 is a flow chart of an image processing method in an embodiment of the present invention;

[0023] Figure 2 A schematic diagram of a bad pixel truncation outline including a bad pixel in an embodiment of the present invention;

[0024] Figure 3 Schematic diagram of bad pixel truncation contour fitting in an embodiment of the present invention;

[0025] Figure 4 is a schematic diagram of the structure of an image processing device in an embodiment of the present invention;

[0026] Figure 5 is a schematic diagram of the structure of an image processing device in an embodiment of the present invention;

[0027] Figure 6 4 is a structural diagram of a computer-readable storage medium in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The embodiments of the present invention solve the technical problem of incomplete imaging when there are bad pixels in the image sensor by providing an image processing device, an image sensor module, an image processing method and an apparatus.

[0029] The technical solution provided by the embodiment of the present invention is to solve the above technical problems, and the overall idea is as follows:

[0030] A sample image to be processed including bad pixels is obtained from the original image to reduce the number of pixels, so as to facilitate the determination of a bad pixel truncation contour including bad pixels from the sample image to be processed. Then, a first pixel point is selected from the bad pixel truncation contour to fit the bad pixel truncation contour so that the bad pixel truncation contour is continuous, and then the fitted bad pixel truncation contour can divide the sample image to be processed into a plurality of sample image units to be processed. Finally, the bad pixels are filled using a second pixel point selected from the plurality of sample image units to be processed, so as to obtain a target image with complete pixels.

[0031] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0032] First of all, the term "and / or" in this article is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0033] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein.

[0034] In a first aspect, the present invention provides an image processing method through an embodiment of the present invention, which can be applied to an image sensor with bad pixels. In the production process of the image sensor, bad pixels may be generated due to the influence of the production process equipment, or in the assembly / use process of the image sensor, when it is exposed to external force or strong light, the image sensor is also prone to bad pixels.

[0035] When a bad pixel is generated in an image sensor, no digital signal is generated at the bad pixel, so the imaged image also has bad pixels, which affects the image quality. By applying the image processing method provided by the embodiment of the present invention, even if there are bad pixels in the image sensor, the final imaged pixels can be made complete, which can reduce the imaging impact of the bad pixel to a certain extent and improve the image quality of the image sensor.

[0036] See also Figure 1 As shown, the image processing method may include the following steps:

[0037] Step S101: obtaining a sample image to be processed including bad pixels from an original image.

[0038] Specifically, imaging can be performed based on an image sensor with bad pixels to obtain an original image, so that a sample image to be processed including bad pixels can be obtained from the original image based on the coordinate information of the bad pixels.

[0039] The coordinate information of the bad pixel can be obtained in advance. Specifically, the coordinates of the bad pixel can be recorded in the detection link of the image sensor, so as to establish the coordinate information of the bad pixel of the image sensor in advance. Of course, different reference images can also be obtained based on the image sensor. Since these reference images also include pixel bad pixels, the coordinate information of the pixel bad pixel can be determined based on these different reference images.

[0040] In order to reduce the amount of pixel processing in the later stage and the amount of calculation for image processing, a sample image to be processed including the bad pixel can be obtained from the original image based on the coordinate information of the bad pixel. Specifically, the bad pixel on the original image can be located by using the coordinate information of the bad pixel, and then the bad pixel and its surrounding pixels can be intercepted based on a preset sampling size to obtain the sample image to be processed.

[0041] In the specific implementation process, the preset sampling size can be set according to the actual situation. The larger the preset sampling size, the more pixels are processed in the later stage. In an optional implementation, the preset sampling size can be a square, and the side length can be any value between 60 pixels and 80 pixels. For example, the preset sampling size can be a square, and the side length of the square can be set to 70 pixels.

[0042] Step S102: Determine a bad pixel truncation contour including bad pixels from the sample image to be processed.

[0043] In order to find the bad pixel truncation contour including the bad pixel, the original image or the sample image to be processed can be preprocessed in advance. The preprocessing can include binarization processing, open-loop processing, closed-loop processing and noise removal processing.

[0044] In a specific implementation process, the original image may be first binarized, and the binarized image may be gray-scaled, and the gray-scale image may be open-loop and closed-loop processed using functions in Open CV to filter out image noise.

[0045] In order to reduce the amount of calculation in the preprocessing process, it is also possible to only perform binarization processing on the sample image to be processed, and perform grayscale processing on the binarized sample image, and use the functions in Open CV to perform open-loop and closed-loop processing on the grayscale sample image to filter out noise in the sample image to be processed.

[0046] Based on the coordinate information of the bad pixel, the coordinate information of the bad pixel is determined, and the image connected domain is used to analyze the sample image to be processed to determine the bad pixel truncation contour including the bad pixel. The image connected domain analysis can be specifically performed with the help of related functions in Open CV.

[0047] Step S103: Selecting a first pixel point from the bad pixel truncation contour to fit the bad pixel truncation contour.

[0048] Specifically, the number of pixels required to fit the bad pixel truncation contour can be determined based on the size of the bad pixel, and based on the number of pixels, the first pixel can be selected from the contour segment of the bad pixel truncation contour that does not include the bad pixel.

[0049] Generally, if Figure 2 As shown, the bad pixel will divide the bad pixel truncation contour into two segments. After determining the number of pixels required to fit the bad pixel truncation contour, the first pixel with half the number of pixels can be selected from the two contour segments.

[0050] For example, if the number of pixel points is 6, then 3 first pixel points may be selected on one contour segment and 3 first pixel points may be selected on another contour segment.

[0051] After selecting the first pixel point, the first pixel point is used to fit the contour segment including the pixel bad point in the bad point truncation contour.

[0052] In the specific implementation process, the line fitting algorithm in Open CV can be used to fit the contour segment including the pixel bad point in the bad point truncation contour to obtain the complete bad point truncation contour, such as Figure 3 As shown in FIG. 1 , the fitted bad point truncation contour divides the sample image to be processed into multiple sample image units to be processed.

[0053] It should be noted that after the bad pixel is fitted in step S103, a line segment will be filled in the bad pixel, but the shape of the bad pixel is generally not a line segment, so there will be unfilled parts, that is, multiple sample image units to be processed may still include bad pixels. The following step S104 is to fill the remaining part of the bad pixel that fits the bad pixel truncation contour to completely fill the bad pixel.

[0054] Step S104: fill in the bad pixels with the second pixel points selected from the plurality of sample image units to be processed, so as to obtain a target image with complete pixels.

[0055] Specifically, a bad pixel compensation area centered on the bad pixel can be determined from the sample image unit to be processed, and then a second pixel is selected in the bad pixel compensation area, and the bad pixel is filled with the second pixel.

[0056] The shape of the bad pixel compensation area can be a rectangle centered on the bad pixel, or a circle; the size of the bad pixel compensation area can be set according to actual conditions, and in some embodiments, the number of pixels included in the bad pixel compensation area can be 64 to 144. For example, if the shape of the bad pixel compensation area is a square, the side length of the bad pixel compensation area can be set to 10 pixels, that is, the number of pixels included in the bad pixel compensation area is 100.

[0057] Regarding how to select the second pixel in the bad pixel compensation area, specifically, the reference texture of the area where the bad pixel is located can be determined first, and then based on the reference texture, the second pixel is selected from the bad pixel compensation area, and finally the second pixel is subjected to difference processing to obtain the compensation pixel, and the bad pixel is filled with the compensation pixel.

[0058] In the specific implementation process, the texture of the area around the pixel bad point can be calculated by Open CV technology to obtain a reference texture, and the reference texture is used to calculate the texture information required for the pixel bad point, and a second pixel matching the texture information is selected from the bad point compensation area, and then the pixel bad point can be filled. Specifically, the pixel in the bad point compensation area that is the same as the reference texture can be selected as the second pixel, or the pixel with a high similarity to the reference texture can be selected as the second pixel.

[0059] It is understandable that the number of selected second pixels can also be determined according to the number of pixels remaining after the pixel bad points. In order to make the selected second pixels fill the pixel bad points more naturally, the second pixels can be difference processed to obtain compensation pixels, and the compensation pixels are used to fill the pixel bad points.

[0060] After the bad pixels are filled with the first pixel point and the second pixel point, the bad pixels in the original image are restored to display pixels, thereby obtaining a target image with complete pixels.

[0061] In a second aspect, based on the same inventive concept, the present invention provides an image processing device through an embodiment of the present invention, which can be applied to an image sensor with bad pixels.

[0062] See also Figure 4 As shown, the image processing device may include:

[0063] The image bad pixel location unit 401 is used to obtain a sample image to be processed including bad pixels from the original image.

[0064] The bad pixel contour determining unit 402 is used to determine the bad pixel truncation contour including the bad pixel from the sample image to be processed.

[0065] The first pixel filling unit 403 is used to select a first pixel point from the bad pixel truncation contour to fit the bad pixel truncation contour, wherein the fitted bad pixel truncation contour divides the sample image to be processed into a plurality of sample image units to be processed.

[0066] The second pixel filling unit 404 is used to fill in the bad pixels with the second pixel points selected from the plurality of sample image units to be processed, so as to obtain a target image with complete pixels.

[0067] As an optional implementation manner, the image bad pixel locating unit 401 includes:

[0068] An image acquisition subunit, used for performing imaging based on an image sensor with bad pixels to obtain an original image;

[0069] The bad pixel locating subunit is used to obtain a sample image to be processed including bad pixels from the original image based on the coordinate information of the bad pixels.

[0070] As an optional implementation manner, the bad pixel locating subunit is specifically used for:

[0071] The bad pixel on the original image is located using the coordinate information of the bad pixel; based on the preset sampling size, the bad pixel and its surrounding pixels are intercepted to obtain the sample image to be processed.

[0072] As an optional implementation manner, the first pixel filling unit 403 is specifically configured to:

[0073] Based on the size of the pixel bad point, the number of pixel points required to fit the bad point truncation contour is determined, and based on the number of pixel points, the first pixel point is selected from the contour segment that does not include the pixel bad point in the bad point truncation contour; using the first pixel point, the contour segment that includes the pixel bad point in the bad point truncation contour is fitted.

[0074] As an optional implementation manner, the second pixel filling unit 404 includes:

[0075] The pixel selection area determination subunit is used to determine a bad pixel compensation area centered on a bad pixel from the sample image unit to be processed;

[0076] The pixel filling subunit is used to select the second pixel point in the bad pixel compensation area and use the second pixel point to fill the pixel bad pixel.

[0077] As an optional implementation manner, the pixel filling subunit is specifically used for:

[0078] Determine the reference texture of the area where the pixel bad point is located; based on the reference texture, select a second pixel point from the bad point compensation area; perform difference processing on the second pixel point to obtain a compensation pixel, and use the compensation pixel to fill the pixel bad point.

[0079] Since the image processing method introduced in this embodiment is a method adopted by the image processing device in the embodiment of the present invention, based on the image processing method introduced in the embodiment of the present invention, a person skilled in the art can understand the specific implementation of the method in the embodiment and its various variations, so how the method implements the method in the embodiment of the present invention is not described in detail here. As long as a person skilled in the art implements the method adopted by the image processing device in the embodiment of the present invention, it belongs to the scope of protection of the present invention.

[0080] In a third aspect, the present invention provides an image processing device through an embodiment of the present invention, which can be used for an image sensor with bad pixels.

[0081] refer to Figure 5 As shown, the image processing device provided by the embodiment of the present invention includes: a memory 501, a processor 502, and an image algorithm stored in the memory and executable on the processor 502. The processor 502 implements any implementation of the above-mentioned image processing method when executing the image algorithm.

[0082] Among them, Figure 5In the embodiment of the present invention, a bus architecture (represented by bus 500) is shown, which may include any number of interconnected buses and bridges, and bus 500 links various circuits including one or more processors represented by processor 502 and memory represented by memory 501. Bus 500 may also link various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. Bus interface 505 provides an interface between bus 500 and receiver 503 and transmitter 504. Receiver 503 and transmitter 504 may be the same element, namely a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 502 is responsible for managing bus 500 and general processing, while memory 501 may be used to store data used by processor 502 when performing operations.

[0083] In a fourth aspect, based on the same concept, the present invention provides an image sensor module through an embodiment of the present invention, including the image processing device in the third aspect, and an image sensor electrically connected to the image processing device.

[0084] In the fifth aspect, based on the same inventive concept, Figure 6 As shown, an embodiment of the present invention provides a computer-readable storage medium 600 on which an image algorithm 601 is stored. When the image algorithm 601 is executed by a processor, any implementation of the above-mentioned image processing method is implemented.

[0085] The technical solutions in the above embodiments of the present invention have at least the following technical effects or advantages:

[0086] By filling the original image with bad pixels, even if the image sensor has bad pixels, an image with complete pixels can still be obtained, thereby improving the imaging quality of the image sensor with bad pixels.

[0087] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable code.

[0088] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer instructions. These computer instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0089] These computer instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0090] These computer instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0091] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0092] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. An image processing method, characterized in that: include: Obtain a sample image to be processed including bad pixels from the original image; Determine a bad pixel truncation contour including the bad pixel from the sample image to be processed; Selecting a first pixel point from the bad pixel truncation contour to fit the bad pixel truncation contour, wherein the fitted bad pixel truncation contour divides the sample image to be processed into a plurality of sample image units to be processed; Filling the pixel bad points with second pixel points selected from the plurality of sample image units to be processed to obtain a target image with complete pixels; The selecting a first pixel point from the bad pixel truncation contour to fit the bad pixel truncation contour includes: Based on the size of the pixel bad point, determine the number of pixel points required to fit the bad point truncation contour, and based on the number of pixel points, select a first pixel point from a contour segment of the bad point truncation contour that does not include the pixel bad point; Using the first pixel point, fitting the contour segment including the pixel bad point in the bad point truncation contour; The step of filling the pixel bad points with the second pixel points selected from the plurality of sample image units to be processed comprises: Determining a bad pixel compensation area centered on the bad pixel from the sample image unit to be processed; Selecting the second pixel point in the bad pixel compensation area, and using the second pixel point to fill the pixel bad pixel; The step of selecting the second pixel point in the bad pixel compensation area and using the second pixel point to fill the bad pixel point includes: Determine a reference texture of the area where the pixel defect is located; Based on the reference texture, selecting a second pixel point from the bad pixel compensation area; Perform difference processing on the second pixel point to obtain a compensation pixel, and use the compensation pixel to fill the pixel defect.

2. The method according to claim 1, characterized in that The step of obtaining a sample image to be processed including bad pixels from an original image comprises: Perform imaging based on an image sensor with bad pixels to obtain the original image; Based on the coordinate information of the bad pixel, a sample image to be processed including the bad pixel is obtained from the original image.

3. The method according to claim 2, characterized in that The step of obtaining a sample image to be processed including the pixel bad point from the original image based on the coordinate information of the bad point comprises: Using the coordinate information of the bad pixel, locating the bad pixel on the original image; Based on a preset sampling size, the bad pixel and its surrounding pixels are intercepted to obtain the sample image to be processed.

4. An image processing device, characterized in that: include: An image bad pixel locating unit, used for obtaining a sample image to be processed including bad pixels from an original image; A bad pixel contour determining unit, used for determining a bad pixel truncation contour including the bad pixel from the sample image to be processed; A first pixel filling unit is used to select a first pixel point from the bad pixel truncation contour to fit the bad pixel truncation contour, wherein the fitted bad pixel truncation contour divides the sample image to be processed into a plurality of sample image units to be processed; The first pixel filling unit is specifically used to: Based on the size of the pixel bad point, determine the number of pixels required to fit the bad point truncation contour, and based on the number of pixels, select a first pixel from the contour segment that does not include the pixel bad point in the bad point truncation contour; use the first pixel to fit the contour segment that includes the pixel bad point in the bad point truncation contour; A second pixel filling unit is used to fill the pixel bad points with second pixel points selected from the plurality of sample image units to be processed, so as to obtain a target image with complete pixels; The second pixel filling unit comprises: The pixel selection area determination subunit is used to determine a bad pixel compensation area centered on a bad pixel from the sample image unit to be processed; A pixel filling subunit, used for selecting the second pixel point in the bad pixel compensation area, and filling the pixel bad pixel with the second pixel point; The pixel filling subunit is specifically used for: Determine the reference texture of the area where the pixel bad point is located; based on the reference texture, select a second pixel point from the bad point compensation area; perform difference processing on the second pixel point to obtain a compensation pixel, and use the compensation pixel to fill the pixel bad point.

5. An image processing device, comprising a memory, a processor, and an image algorithm stored in the memory and executable on the processor, characterized in that: When the processor executes the image algorithm, the method described in any one of claims 1 to 3 is implemented.

6. An image sensor module, characterized in that: The invention comprises the image processing device as claimed in claim 5, and an image sensor electrically connected to the image processing device.

7. A computer-readable storage medium having an image algorithm stored thereon, characterized in that: When the image algorithm is executed by a processor, the method described in any one of claims 1 to 3 is implemented.

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