Fixed pattern noise detection method and device for camera module

Abstract

The invention discloses a fixed pattern noise detection method for a camera module. The method comprises the following steps: step 100, obtaining a test image of the camera module; step 200, decomposing the test image into a plurality of single-channel images according to the number of channels of the test image; step 300, calculating single-row pixel values and single-column pixel values of all the single-channel images; and step 400, determining whether the test image has fixed pattern noise according to the single-row pixel values and the single-column pixel values of all the single-channel images. The fixed pattern noise detection method can improve the detection and identification precision of the fixed pattern noise. The invention further discloses a fixed pattern noise detection device for the camera module, which is used for realizing the method.

Description

Technical Field

[0001] The present invention relates to the field of camera module detection, and in particular to a fixed pattern noise detection method and device for a camera module. Background Art

[0002] A camera module is a modular component that integrates an image sensor, an optical lens, an image processing chip, and other related circuits. This modular design makes the integration of the camera much simpler and can be easily integrated into a variety of devices such as smartphones, tablets, smart home devices, industrial vision systems, etc.

[0003] Fixed Pattern Noise (FPN) is a type of noise that appears at a fixed position in an image. This noise will appear repeatedly at the same position in multiple shots or captured images. Its causes include uneven image sensor response rates, optical lens defects, image sensor manufacturing errors, etc. Therefore, camera modules are tested for fixed pattern noise before leaving the factory to screen out defective products and prevent them from flowing into end customers.

[0004] For example, a Chinese patent application numbered CN201580052995.9 discloses a method for calculating fixed pattern noise based on an infrared camera device, wherein the infrared camera device comprises: an imaging element comprising a plurality of infrared detection pixels arranged in a two-dimensional manner; and an aperture arranged on a side closer to the subject than the imaging element. The method for calculating fixed pattern noise comprises: a fixed pattern noise calculation step, wherein when shooting a dynamic image, first camera image data obtained by shooting with the imaging element taking the F value of the aperture as a first value and second camera image data obtained by shooting with the imaging element taking the F value of the aperture as a second value are obtained, and the fixed pattern noise contained in the camera image data obtained by shooting with the imaging element is calculated based on the first camera image data and the second camera image data obtained and the first value and the second value.

[0005] However, the image sensor has multiple channels, each of which is only responsible for recording the brightness information of the corresponding color. When the image sensor has a color cast problem, the channel corresponding to the color cast will have a high brightness problem, which will lead to a large brightness difference between this channel and other channels, and it will be misjudged as noise.

[0006] In addition, the existing fixed pattern noise test method can only detect and identify the stripe noise that appears in a whole row or a whole column. When the fixed pattern noise does not appear in a whole row or a whole column of stripes, it cannot be detected and identified. Summary of the invention

[0007] In order to solve the above-mentioned deficiencies in the prior art, the present invention provides a method and device for detecting fixed pattern noise of a camera module, which can improve the detection and recognition accuracy of fixed pattern noise.

[0008] The technical problem to be solved by the present invention is achieved through the following technical solutions: A method for detecting fixed pattern noise of a camera module comprises the following steps: Step 100: Obtain a test image of the camera module; Step 200: Decomposing the test image into multiple single-channel images according to the number of channels of the test image; Step 300: Calculate the single-row pixel values ​​and single-column pixel values ​​of all single-channel images; Step 400: Determine whether the test image has fixed pattern noise based on the single-row pixel values ​​and single-column pixel values ​​of all single-channel images.

[0009] Further, in step 400, the steps of determining whether the test image has fixed pattern noise according to the single-row pixel values ​​and single-column pixel values ​​of all single-channel images are as follows: Step 410: grouping all single-row pixel values ​​and single-column pixel values ​​of each single-channel image to obtain multiple row data groups and column data groups, each row data group has A adjacent single-row pixel values, each column data group has A adjacent single-column pixel values, A≥2; Step 420: Calculate the average values ​​of all row data groups and column data groups to obtain multiple row group average values ​​and column group average values; Step 430: Calculate the difference between the average values ​​of three adjacent row groups and the average values ​​of three adjacent column groups to obtain a plurality of row group difference values ​​and column group difference values; Step 440: Filter out the maximum values ​​of all row grouping differences and column grouping differences to obtain the row grouping maximum difference and the column grouping maximum difference; Step 450: repeating steps 410 to 440 until the maximum difference values ​​of the row groups and the maximum difference values ​​of the column groups of all single-channel images are obtained; Step 460: Calculate the row discrete value and column discrete value of the test image according to the row grouping maximum difference and column grouping maximum difference of all single-channel images; Step 480: Determine whether the test image has fixed pattern noise according to the row discrete value, the column discrete value and the preset discrete threshold.

[0010] Furthermore, in step 480, when judging whether the test image has fixed pattern noise, if the row discrete value or the column discrete value is greater than the preset discrete threshold, it is determined that the test image has fixed pattern noise; if both the row discrete value and the column discrete value are less than the preset discrete threshold, it is determined that the test image does not have fixed pattern noise.

[0011] Furthermore, between step 460 and step 480, the following steps are also included: Step 470: Repeat steps 410 to 460 to obtain multiple row discrete values ​​and column discrete values ​​of the test image when the single-channel image is grouped using different A values.

[0012] Furthermore, in step 480, when judging whether the test image has fixed pattern noise, if one of all row discrete values ​​or one of all column discrete values ​​is greater than a preset discrete threshold, it is determined that the test image has fixed pattern noise; if all row discrete values ​​and column discrete values ​​are less than the preset discrete threshold, it is determined that the test image does not have fixed pattern noise.

[0013] Further, in step 400, the steps of determining whether the test image has fixed pattern noise according to the single-row pixel values ​​and single-column pixel values ​​of all single-channel images are as follows: Step 410: Calculate the row discrete value and column discrete value of each single-channel image according to all single-row pixel values ​​and single-column pixel values; Step 420: Determine whether fixed pattern noise exists in each single-channel image according to the row discrete value, the column discrete value and the preset discrete threshold; Step 430: repeating steps 410 and 420 to determine whether all single-channel images have fixed pattern noise; Step 440: Based on the noise conditions of all single-channel images, comprehensively determine whether the test image has fixed pattern noise.

[0014] Furthermore, in step 440, when judging whether the test image has fixed pattern noise, as long as one of all the single-channel images is determined to have fixed pattern noise, it is determined that the test image has fixed pattern noise; and only when all the single-channel images are determined to have no fixed pattern noise, it is determined that the test image has no fixed pattern noise.

[0015] Furthermore, the test image is obtained by photographing the camera module in a dark field environment.

[0016] Furthermore, when shooting, the brightness gain value of the camera module is increased.

[0017] A fixed pattern noise detection device for a camera module includes a processor and a memory electrically connected to the processor, wherein the memory stores a computer program for execution by the processor, and when the processor executes the computer program, the processor performs the above-mentioned fixed pattern noise detection method.

[0018] The present invention has the following beneficial effects: The fixed pattern noise detection method of the present invention is to decompose the test image of the camera module into multiple single-channel images according to the number of channels, and then calculate the single-row pixel value and the single-column pixel value of all the single-channel images separately to determine whether the test image has fixed pattern noise. When the brightness of a certain channel of the camera module is high due to the color deviation of the image sensor, since each single-channel image has the brightness information of only one color, the single-channel images corresponding to other colors are not affected by the single-channel images corresponding to the color deviation when calculating the single-row pixel value and the single-column pixel value, thereby improving the detection and recognition accuracy of the fixed pattern noise. The fixed pattern noise detection method of the present invention determines whether all single-channel images have fixed pattern noise based on the discreteness of the column pixel values ​​and the row pixel values ​​of all single-channel images, rather than based on the traditional edge detection value. When the fixed pattern noise does not appear as a whole row or a whole column of stripes, the fixed pattern noise can also be detected and identified based on the discreteness, which can improve the detection and identification accuracy of the fixed pattern noise. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a block diagram of the steps of the fixed pattern noise detection method provided by the present invention.

[0020] Figure 2 This is a block diagram of step 400 in the fixed pattern noise detection method provided by the present invention.

[0021] Figure 3 This is a block diagram of step 400 in another fixed pattern noise detection method provided by the present invention.

[0022] Figure 4 This is a block diagram of step 400 in another fixed pattern noise detection method provided by the present invention. DETAILED DESCRIPTION

[0023] The present invention is described in detail below in conjunction with the accompanying drawings and embodiments, examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0024] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside" and "outside" etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0025] In addition, the terms "first", "second", and "third" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first", "second", and "third" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0026] In the present invention, unless otherwise clearly specified and limited, the terms "install", "connect", "connect", "fix", "set" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or an indirect connection through an intermediate medium, or it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0027] Embodiment 1 like Figure 1 As shown, a method for detecting fixed pattern noise of a camera module comprises the following steps: Step 100: Obtain a test image of the camera module.

[0028] In step 100, the test image is obtained by shooting the camera module in a dark field environment, which refers to an environment with very weak light or almost no light. Since the non-uniformity of dark current is the main source of fixed pattern noise, in the dark field environment, the image sensor of the camera module is only affected by the dark current, and the noise influence of external light on the image sensor can be excluded, thereby more accurately evaluating the non-uniformity of the dark signal of each pixel in the image sensor.

[0029] Specifically, when shooting, a black baffle can be set in front of the optical lens of the camera module to block external light from entering the optical lens, thereby creating the dark field environment. Of course, the dark field environment is a commonly used test environment for the camera module, and there are many ways to create the dark field environment, which should not be limited to this embodiment.

[0030] Preferably, when shooting, the brightness gain value of the camera module is increased. By increasing the brightness gain value, the signal strength of the image sensor can be increased, including the noise signal, so that the fixed pattern noise in the test image is more obvious.

[0031] Optimally, the brightness gain value can be set to the maximum value that the camera module can support. Of course, the brightness gain value can also be set to a suitable value according to the test requirements, as long as the fixed pattern noise in the test image can be clearly identified.

[0032] Preferably, there are multiple test images to eliminate the test error of a single image and improve the test accuracy.

[0033] Step 200: Decompose the test image into multiple single-channel images according to the number of channels of the test image.

[0034] In step 200, the image sensor of the camera module has multiple channels, each channel is only responsible for recording the brightness information of the corresponding color, and the single-channel image only has the brightness information recorded by one channel in the test image.

[0035] Taking the common Bayer array pixel structure as an example, each pixel array in the image sensor is composed of four pixels RGGB, namely one red pixel, one blue pixel and two green pixels, corresponding to one red channel, one blue channel and two green channels respectively, that is, one pixel in the pixel array represents one channel, even if there are multiple pixels of the same color in the pixel array, they represent different channels respectively.

[0036] Step 300: Calculate the single-row pixel values ​​and single-column pixel values ​​of all single-channel images.

[0037] In step 300, all single-channel images are composed of m rows and n columns of pixels of corresponding colors, where m, n ≥ 2. For example, the red channel image is composed of m rows and n columns of red pixels, the blue channel image is composed of m rows and n columns of blue pixels, and the green channel image is composed of m rows and n columns of green pixels.

[0038] Then, when calculating the single-row pixel value and single-column pixel value of each single-channel image, the brightness values ​​of all pixels in the first pixel row are added, and then divided by the pixel quantity of the first pixel row, so as to obtain the single-row pixel value R1 of the first pixel row, and then the brightness values ​​of all pixels in the second pixel row are added, and then divided by the pixel quantity of the second pixel row, so as to obtain the single-row pixel value R2 of the second pixel row... and so on, the brightness values ​​of all pixels in the m-th pixel row are added, and then divided by the pixel quantity of the m-th pixel row, so as to obtain the single-row pixel value Rm of the m-th pixel row; then the brightness values ​​of all pixels in the first pixel column are added, and then divided by the pixel quantity of the first pixel column, so as to obtain the single-column pixel value C1 of the first pixel column, and then the brightness values ​​of all pixels in the second pixel column are added, and then divided by the pixel quantity of the second pixel column, so as to obtain the single-column pixel value C2 of the second pixel column... and so on, the brightness values ​​of all pixels in the n-th pixel column are added, and then divided by the pixel quantity of the n-th pixel column, so as to obtain the single-column pixel value Cn of the n-th pixel column.

[0039] The above calculations are performed on all single-channel images in sequence to obtain the single-row pixel values ​​and single-column pixel values ​​of all single-channel images in sequence. If the image sensor adopts a Bayer array, it is necessary to obtain the single-row pixel values ​​and single-column pixel values ​​of a red channel image, the single-row pixel values ​​and single-column pixel values ​​of a blue channel image, and the single-row pixel values ​​and single-column pixel values ​​of two green channel images in sequence.

[0040] Step 400: Determine whether the test image has fixed pattern noise based on the single-row pixel values ​​and single-column pixel values ​​of all single-channel images.

[0041] The fixed pattern noise detection method of the present invention decomposes the test image of the camera module into multiple single-channel images according to the number of channels, and then calculates the single-row pixel values ​​and the single-column pixel values ​​of all the single-channel images separately to determine whether the test image has fixed pattern noise. When the brightness of a certain channel of the camera module is high due to the color deviation of the image sensor, since each single-channel image has brightness information of only one color, the single-channel images corresponding to other colors are not affected by the single-channel images corresponding to the color deviation colors when calculating the single-row pixel values ​​and the single-column pixel values, thereby improving the detection and recognition accuracy of the fixed pattern noise.

[0042] In addition, the fixed pattern noise detection method of the present invention determines whether fixed pattern noise exists in all single-channel images based on the discreteness of the column pixel values ​​and the row pixel values ​​of all single-channel images, rather than based on traditional edge detection values. When the fixed pattern noise does not appear as a whole row or a whole column of stripes, the fixed pattern noise can also be detected and identified based on the discreteness, thereby improving the detection and identification accuracy of the fixed pattern noise.

[0043] Embodiment 2 As an optimization solution of the first embodiment, in this embodiment, if Figure 2 As shown, in step 400, the steps of determining whether the test image has fixed pattern noise according to the single-row pixel values ​​and single-column pixel values ​​of all single-channel images are as follows: Step 410: Group all single-row pixel values ​​and single-column pixel values ​​of each single-channel image to obtain multiple row data groups and column data groups, each row data group has A adjacent single-row pixel values, each column data group has A adjacent single-column pixel values, and A≥2.

[0044] In step 410, all single-row pixel values ​​of each single-channel image are divided into multiple row data groups, each row data group has A adjacent single-row pixel values, and all single-column pixel values ​​of each single-channel image are divided into multiple column data groups, each column data group has A adjacent single-column pixel values.

[0045] Since the number of pixel rows and columns of a single-channel image is not necessarily the same, the number of the row data groups and the number of the column data groups are not necessarily the same.

[0046] Preferably, there are no repeated single-row pixel values ​​between all row data groups, and there are no repeated single-column pixel values ​​between all column data groups.

[0047] Step 420: Calculate the average values ​​of all row data groups and column data groups to obtain multiple row group average values ​​and column group average values.

[0048] In step 420, all single-row pixel values ​​in each row data group are added together and then divided by A to obtain the row group average of each row data group. One row group average corresponds to one row data group. All column pixel values ​​in each column data group are added together and then divided by A to obtain the column group average of each column data group. One column group average corresponds to one column data group.

[0049] Step 430: Calculate the difference between three adjacent row group average values ​​and column group average values ​​to obtain a plurality of row group difference values ​​and column group difference values.

[0050] In step 323, each row group difference = 2*R'(middle) + R'(front) + R'(back), R'(front), R'(middle), R'(back) are respectively the average values ​​of the first, middle, and last row groups among the three adjacent row group average values, and the column group difference = 2*C'(middle) + C'(front) + C'(back), C'(front), C'(middle), C'(back) are respectively the average values ​​of the first, middle, and last column groups among the three adjacent column group average values.

[0051] Preferably, when calculating the row grouping differences and column grouping differences, the row grouping average values ​​and column grouping average values ​​are not reused.

[0052] Step 440: Filter out the maximum values ​​of all row grouping differences and column grouping differences to obtain the row grouping maximum difference and the column grouping maximum difference.

[0053] In step 440, all row grouping differences of each single-channel image are compared, and the maximum value is screened out as the maximum row grouping difference of the single-channel image; all column grouping differences of each single-channel image are compared, and the maximum value is screened out as the maximum column grouping difference of the single-channel image.

[0054] Step 450: Repeat steps 410 to 440 until the maximum difference values ​​of the row groups and the maximum difference values ​​of the column groups of all single-channel images are obtained.

[0055] Step 460: Calculate the row discrete value and column discrete value of the test image according to the row grouping maximum difference and column grouping maximum difference of all single-channel images.

[0056] In step 460, the row discrete value is calculated based on the maximum difference of the row groupings of all single-channel images, such as the standard deviation or the mean difference of the maximum differences of all row groupings, and the column discrete value is calculated based on the maximum difference of the column groupings of all single-channel images, such as the standard deviation or the mean difference of the maximum differences of all column groupings.

[0057] Step 480: Determine whether the test image has fixed pattern noise according to the first discrete value, the column discrete value and the preset discrete threshold.

[0058] In step 480, when judging whether the test image has fixed pattern noise, if the row discrete value or the column discrete value is greater than the preset discrete threshold, it is determined that the test image has fixed pattern noise; if both the row discrete value and the column discrete value are less than the preset discrete threshold, it is determined that the test image does not have fixed pattern noise.

[0059] Embodiment 3 As an optimization solution of the second embodiment, in this embodiment, if Figure 3 As shown, between step 460 and step 480, the following steps are also included: Step 470: Repeat steps 410 to 460 to obtain multiple row discrete values ​​and column discrete values ​​of the test image when the single-channel image is grouped using different A values.

[0060] At this time, in step 480, when judging whether the test image has fixed pattern noise, if one of all row discrete values ​​or one of all column discrete values ​​is greater than the preset discrete threshold, it is determined that the test image has fixed pattern noise; if all row discrete values ​​and column discrete values ​​are less than the preset discrete threshold, it is determined that the test image does not have fixed pattern noise.

[0061] Preferably, the A value used each time the single-channel images are grouped is a positive integer multiple of the A value used last time the single-channel images were grouped, and the number of cycles is about 3-5 times.

[0062] Embodiment 4 As another optimization solution of the first embodiment, in this embodiment, if Figure 4 As shown, in step 400, the steps of determining whether the test image has fixed pattern noise according to the single-row pixel values ​​and single-column pixel values ​​of all single-channel images are as follows: Step 410: Calculate the row discrete value and column discrete value of each single-channel image according to all single-row pixel values ​​and single-column pixel values.

[0063] In step 410, the row discrete value is calculated based on all single-row pixel values ​​of each single-channel image, such as the standard deviation or mean difference of all single-row pixel values, and the column discrete value is calculated based on all single-column pixel values ​​of each single-channel image, such as the standard deviation or mean difference of all single-column pixel values.

[0064] Step 420: Determine whether fixed pattern noise exists in each single-channel image according to the row discrete value, the column discrete value and the preset discrete threshold.

[0065] In step 420, when judging whether fixed pattern noise exists in each single-channel image, if the row discrete value or the column discrete value is greater than the preset discrete threshold, it is determined that fixed pattern noise exists in each single-channel image; if both the row discrete value and the column discrete value are less than the preset discrete threshold, it is determined that fixed pattern noise does not exist in each single-channel image.

[0066] Step 430: Repeat steps 410 and 420 to determine whether fixed pattern noise exists in all single-channel images.

[0067] In step 430 , if the image sensor adopts the Bayer array, it is necessary to sequentially determine whether a red channel image, a blue channel image, and two green channel images have fixed pattern noise.

[0068] Step 440: Based on the noise conditions of all single-channel images, comprehensively determine whether the test image has fixed pattern noise.

[0069] In step 440, when judging whether the test image has fixed pattern noise, as long as one of all the single-channel images is determined to have fixed pattern noise, it is determined that the test image has fixed pattern noise. Only when all the single-channel images are determined to have no fixed pattern noise, it is determined that the test image has no fixed pattern noise.

[0070] Embodiment 5 A fixed pattern noise detection device for a camera module includes a processor and a memory electrically connected to the processor, wherein the memory stores a computer program for execution by the processor, and when the processor executes the computer program, the fixed pattern noise detection method described in Example 1, Example 2, Example 3 or Example 4 is performed.

[0071] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention rather than to limit them. Although the embodiments of the present invention have been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the embodiments of the present invention can still be modified or replaced by equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for detecting fixed pattern noise of a camera module, characterized in that: The steps include: Step 100: Obtain a test image of the camera module; Step 200: Decomposing the test image into multiple single-channel images according to the number of channels of the test image; Step 300: Calculate the single-row pixel values ​​and single-column pixel values ​​of all single-channel images; Step 400: Determine whether the test image has fixed pattern noise based on the single-row pixel values ​​and single-column pixel values ​​of all single-channel images.

2. The fixed pattern noise detection method according to claim 1, characterized in that: In step 400, the steps of determining whether the test image has fixed pattern noise according to the single-row pixel values ​​and single-column pixel values ​​of all single-channel images are as follows: Step 410: grouping all single-row pixel values ​​and single-column pixel values ​​of each single-channel image to obtain multiple row data groups and column data groups, each row data group has A adjacent single-row pixel values, each column data group has A adjacent single-column pixel values, A≥2; Step 420: Calculate the average values ​​of all row data groups and column data groups to obtain multiple row group average values ​​and column group average values; Step 430: Calculate the difference between the average values ​​of three adjacent row groups and the average values ​​of three adjacent column groups to obtain a plurality of row group difference values ​​and column group difference values; Step 440: Filter out the maximum values ​​of all row grouping differences and column grouping differences to obtain the maximum row grouping difference and the maximum column grouping difference; Step 450: repeating steps 410 to 440 until the maximum difference values ​​of the row groups and the maximum difference values ​​of the column groups of all single-channel images are obtained; Step 460: Calculate the row discrete value and column discrete value of the test image according to the row grouping maximum difference and column grouping maximum difference of all single-channel images; Step 480: Determine whether the test image has fixed pattern noise according to the row discrete value, the column discrete value and the preset discrete threshold.

3. The fixed pattern noise detection method according to claim 2, characterized in that: In step 480, when judging whether the test image has fixed pattern noise, if the row discrete value or the column discrete value is greater than the preset discrete threshold, it is determined that the test image has fixed pattern noise; if both the row discrete value and the column discrete value are less than the preset discrete threshold, it is determined that the test image does not have fixed pattern noise.

4. The fixed pattern noise detection method according to claim 2, characterized in that: Between step 460 and step 480, the following steps are also included: Step 470: Repeat steps 410 to 460 to obtain multiple row discrete values ​​and column discrete values ​​of the test image when the single-channel image is grouped using different A values.

5. The fixed pattern noise detection method according to claim 4, characterized in that: In step 480, when judging whether the test image has fixed pattern noise, if one of all row discrete values ​​or one of all column discrete values ​​is greater than a preset discrete threshold, it is determined that the test image has fixed pattern noise; if all row discrete values ​​and column discrete values ​​are less than the preset discrete threshold, it is determined that the test image does not have fixed pattern noise.

6. The fixed pattern noise detection method according to claim 1, characterized in that: In step 400, the steps of determining whether the test image has fixed pattern noise according to the single-row pixel values ​​and single-column pixel values ​​of all single-channel images are as follows: Step 410: Calculate the row discrete value and column discrete value of each single-channel image according to all single-row pixel values ​​and single-column pixel values; Step 420: Determine whether fixed pattern noise exists in each single-channel image according to the row discrete value, the column discrete value and the preset discrete threshold; Step 430: repeating steps 410 and 420 to determine whether all single-channel images have fixed pattern noise; Step 440: Based on the noise conditions of all single-channel images, comprehensively determine whether the test image has fixed pattern noise.

7. The fixed pattern noise detection method according to claim 6, characterized in that: In step 440, when judging whether the test image has fixed pattern noise, as long as one of all the single-channel images is determined to have fixed pattern noise, it is determined that the test image has fixed pattern noise. Only when all the single-channel images are determined to have no fixed pattern noise, it is determined that the test image has no fixed pattern noise.

8. The fixed pattern noise detection method according to claim 1, characterized in that: The test image is obtained by photographing the camera module in a dark field environment.

9. The fixed pattern noise detection method according to claim 8, characterized in that: When shooting, the brightness gain value of the camera module is increased.

10. A fixed pattern noise detection device for a camera module, comprising a processor and a memory electrically connected to the processor, wherein the memory stores a computer program for execution by the processor, wherein: When the processor executes the computer program, the fixed pattern noise detection method according to claim 1 is performed.

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