Image noise processing methods, devices, image sensors, and storage media

Abstract

This invention proposes an image noise processing method, apparatus, image sensor, and storage medium. The method includes acquiring a reference column containing reference pixels with preset characteristics in a pixel array; determining the position of the reference column containing fixed-pattern noise from the reference column; and eliminating the fixed-pattern noise of valid pixels in the target column indicated by the position of the reference column in the pixel array. This invention enables noise elimination only for columns of pixels with fixed-pattern noise, avoiding impact on columns without fixed-pattern noise, thus improving noise reduction effectiveness and ensuring image rendering quality.

Description

Technical Field

[0001] This invention relates to the field of image processing technology, and in particular to an image noise processing method, apparatus, image sensor, and storage medium. Background Technology

[0002] Fixed-pattern noise in image sensors, after gain amplification, forms noticeable stripes along the column direction. Eliminating or reducing this noise is one of the important research areas in image sensors. Fixed-pattern noise is basically positively correlated with the sensor gain; increasing the gain will amplify the fixed-pattern noise. Fixed-pattern noise is also related to temperature, with its magnitude varying at different temperatures.

[0003] In related technologies, the average value of the effective pixel and the black row pixels in the same column is usually used to obtain the pixel that eliminates fixed pattern noise.

[0004] This method subtracts all column pixels, which cannot accurately eliminate fixed-pattern noise. It will perform the same operation on columns without fixed-pattern noise. Since the average value of black row pixels is not zero, there will be a loss of effective pixels in columns without fixed-pattern noise, which will affect the overall image quality. Summary of the Invention

[0005] The present invention aims to at least partially solve one of the technical problems in the related art.

[0006] Therefore, the purpose of this invention is to provide an image noise processing method, apparatus, image sensor, and storage medium that can perform noise removal processing only on column pixels with fixed pattern noise, avoiding affecting column pixels without fixed pattern noise, thereby improving the noise removal effect and ensuring the image presentation effect.

[0007] To achieve the above objectives, the image noise processing method proposed in the first aspect of the present invention includes: obtaining a reference column in a pixel array containing a reference pixel with preset features; determining the position of a reference column containing fixed-pattern noise from the reference column; and eliminating the fixed-pattern noise of valid pixels in a target column indicated by the position of the reference column in the pixel array.

[0008] The image noise processing method proposed in the first aspect of the present invention obtains the reference column where the reference pixel with preset characteristics is located in the pixel array, determines the position of the reference column with fixed pattern noise from the reference column, and performs fixed pattern noise elimination processing on the effective pixels in the target column indicated by the position of the reference column. This method can eliminate noise only for the column pixels with fixed pattern noise, avoid affecting the column pixels without fixed pattern noise, improve the noise elimination effect, and ensure the image presentation effect.

[0009] To achieve the above objectives, an image noise processing apparatus according to a second aspect embodiment of the present invention includes: an acquisition module for acquiring a reference column in a pixel array containing a reference pixel having a preset feature; a determination module for determining the position of a reference column containing fixed-pattern noise from the reference column; and a processing module for eliminating the fixed-pattern noise of valid pixels in a target column indicated by the position of the reference column in the pixel array.

[0010] The image noise processing apparatus proposed in the second aspect of the present invention obtains the reference column where the reference pixel with preset characteristics is located in the pixel array, determines the position of the reference column with fixed pattern noise from the reference column, and performs fixed pattern noise elimination processing on the effective pixels in the target column indicated by the position of the reference column. It can perform noise elimination processing only on the column pixels with fixed pattern noise, avoid affecting the column pixels without fixed pattern noise, improve the noise elimination effect, and ensure the image presentation effect.

[0011] To achieve the above objectives, the image sensor proposed in the third aspect of the present invention includes: the image noise processing device proposed in the second aspect of the present invention.

[0012] The image sensor proposed in the third aspect of the present invention obtains the reference column where the reference pixel with preset characteristics is located in the pixel array, determines the position of the reference column with fixed pattern noise from the reference column, and performs fixed pattern noise elimination processing on the effective pixels in the target column indicated by the position of the reference column. This can eliminate noise only for the column pixels with fixed pattern noise, avoid affecting the column pixels without fixed pattern noise, improve the noise elimination effect, and ensure the image presentation effect.

[0013] To achieve the above objectives, a computer-readable storage medium is provided in the fourth aspect of the present invention, which stores a computer program thereon, characterized in that the program, when executed by a processor, implements: the image noise processing method proposed in the first aspect of the present invention.

[0014] The computer-readable storage medium proposed in the fourth aspect of the present invention obtains a reference column containing a reference pixel with preset characteristics in a pixel array, determines the position of the reference column containing fixed-pattern noise from the reference column, and performs fixed-pattern noise elimination processing on the effective pixels in the target column indicated by the position of the reference column. This enables noise elimination processing to be performed only on the column pixels with fixed-pattern noise, avoiding affecting the column pixels without fixed-pattern noise, improving the noise elimination effect, and ensuring the image presentation effect.

[0015] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0016] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0017] Figure 1 This is a schematic flowchart of an image noise processing method proposed in an embodiment of the present invention;

[0018] Figure 2 This is a schematic diagram showing the distribution of black row pixels and effective row pixels in an embodiment of the present invention;

[0019] Figure 3 This is a schematic diagram of pixel data with a fixed pattern of noise in an embodiment of the present invention;

[0020] Figure 4 This is a schematic diagram of pixel data without a fixed pattern of noise in an embodiment of the present invention;

[0021] Figure 5 This is a schematic diagram of the image noise processing device proposed in one embodiment of the present invention;

[0022] Figure 6 This is a schematic diagram of the structure of an image noise processing device proposed in another embodiment of the present invention;

[0023] Figure 7 This is a schematic diagram of the structure of an image sensor proposed in one embodiment of the present invention. Detailed Implementation

[0024] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the invention, and should not be construed as limiting the invention. Rather, embodiments of the invention include all variations, modifications, and equivalents falling within the spirit and scope of the appended claims.

[0025] Figure 1 This is a schematic flowchart of an image noise processing method proposed in an embodiment of the present invention.

[0026] This embodiment illustrates the example of an image noise processing method configured in an image noise processing apparatus.

[0027] In this embodiment, the image noise processing method can be configured in an image noise processing device, which can be located in a server or an electronic device. This application embodiment does not limit this.

[0028] This embodiment uses an image noise processing method configured in an electronic device as an example.

[0029] It should be noted that the execution entity in the embodiments of this application may be, for example, a central processing unit (CPU) in a server or electronic device in terms of hardware, and may be, for example, a related background service in a server or electronic device in terms of software, without limitation.

[0030] The image noise in this embodiment of the invention is column fixed pattern noise in the image sensor.

[0031] Fixed-pattern noise in image sensors, after gain amplification, forms noticeable stripes along the column direction. Eliminating or reducing this noise is one of the important research areas in image sensors. Fixed-pattern noise is basically positively correlated with the sensor gain; increasing the gain will amplify the fixed-pattern noise. Fixed-pattern noise is also related to temperature, with its magnitude varying at different temperatures.

[0032] In related technologies, the average value of the effective pixel and the black row pixels in the same column is usually used to obtain the pixel that eliminates fixed pattern noise.

[0033] This method subtracts all column pixels, which cannot accurately eliminate fixed-pattern noise. It will perform the same operation on columns without fixed-pattern noise. Since the average value of black row pixels is not zero, there will be a loss of effective pixels in columns without fixed-pattern noise, which will affect the overall image quality.

[0034] To address the aforementioned technical problems, this invention provides an image noise processing method. This method involves obtaining a reference column containing reference pixels with preset characteristics within a pixel array, determining the position of the reference column containing fixed-pattern noise from the reference column, and eliminating the fixed-pattern noise of valid pixels in the target column indicated by the position of the reference column. Since the noise elimination process only targets the column pixels with fixed-pattern noise, it avoids affecting column pixels without fixed-pattern noise, thereby improving the noise elimination effect and ensuring the image presentation effect.

[0035] See Figure 1 The method includes:

[0036] S101: Obtain the reference column where the reference pixel with preset characteristics is located in the pixel array.

[0037] In this embodiment of the invention, the reference pixel with preset features is a pixel that cannot sense light and can be called a black row pixel. The difference between a black row pixel and an effective row pixel is that during the manufacturing process, a light-blocking material is filled on top of the black row pixel so that the pixel cannot sense light.

[0038] In this embodiment of the invention, the black row pixels in the pixel array can be referred to as reference pixels.

[0039] See Figure 2 , Figure 2 This is a schematic diagram showing the distribution of black row pixels and effective row pixels in an embodiment of the present invention. It includes: a single pixel 21 in the pixel array, a black pixel row 22 (pixels in the black pixel row 22 can be referred to as black row pixels), and an effective pixel row 23 (pixels in the effective pixel row 23 can be referred to as effective row pixels), wherein the black pixel row 22 is row i, and the effective pixel row 23 is row j.

[0040] In the specific execution process of this embodiment of the invention, the column where the black row of pixels is located can be used as a reference column. The relevant data of the reference column can be obtained and stored in advance. During execution, the pre-stored reference column can be read directly, or the pixel data corresponding to the pixel array can be analyzed and identified in real time to obtain the reference column where the reference pixel with preset characteristics is located in the pixel array, and then the following steps are triggered.

[0041] S102: Determine the location of the reference column containing fixed-pattern noise from the reference column.

[0042] In the specific execution of this embodiment of the invention, after taking the column where the black row pixels are located as the reference column, the position of the reference column containing fixed pattern noise is determined from the reference column.

[0043] Compared to related technologies, the embodiments of the present invention determine the positions of reference columns containing fixed-pattern noise from the reference columns. That is, the embodiments of the present invention first judge each reference column to determine whether there is fixed-pattern noise in each reference column. Not only is the position of the reference column with fixed-pattern noise determined, but the magnitude of the fixed-pattern noise is also determined. Noise cancellation operation is performed only when the column is determined to have column fixed-pattern noise. No related operation is performed on columns that are not determined to have column fixed-pattern noise, thus preserving the authenticity of the valid signal data. Noise cancellation operation is performed only on columns carrying fixed-pattern noise, thus eliminating fixed-pattern noise. At the same time, it avoids introducing errors into the valid pixels of other columns that do not have fixed-pattern noise.

[0044] Specifically, for each reference column, for each first reference pixel in the reference column, a first noise signal of the first reference pixel is determined, and a second noise signal of a second reference pixel in the preceding column of the reference column and a third noise signal of a third reference pixel in the following column of the reference column are determined. Based on the first noise signal, the second noise signal, and the third noise signal, it is determined whether there is fixed pattern noise in the reference column. When it is determined that there is fixed pattern noise in the reference column, the position of the reference column is determined.

[0045] See Figure 3 , Figure 3 This is a schematic diagram of pixel data with a fixed pattern of noise in an embodiment of the present invention. Figure 4 This is a schematic diagram of pixel data without a fixed pattern of noise in an embodiment of the present invention. Figure 3 and Figure 4 In this context, D and D' represent noise signals, S FPN represents fixed-mode noise, and S represents the optical signal.

[0046] In this embodiment of the invention, the calculation logic for determining the existence of fixed-pattern noise is exemplified as follows: Subtract the first column from the second column of the black row pixels. When there is no fixed-pattern noise, since the noise signals in the two columns are identical, 50% of the difference Di2-Di1 has a positive sign and 50% has a negative sign. The error is... Therefore, the more rows of black pixels there are, the more accurate the result.

[0047] However, when fixed-mode noise is present, due to the involvement of SFPN, the signs of Di2-Di1 will no longer be 50% positive and 50% negative, but 100%, where the error is also...

[0048] Therefore, in this embodiment of the invention, for example, to determine whether there is fixed-pattern noise in the second column, the percentage of the number of signs in Di2-Di1 and Di2-Di3 can be used as an effective basis for determining whether there is fixed-pattern noise. When the percentage of signs in Di2-Di1 and Di2-Di3 are all positive or all negative is greater than 1%, the determination is made by using the percentage of signs in Di2-Di1 and Di2-Di3 that are all positive or all negative. When the sign is positive, it can be determined that the second column has fixed-pattern noise; when the sign is negative, it indicates that the fixed-pattern noise in that column is positive, and when the sign is negative, it indicates that the fixed-pattern noise in that column is negative. That is, when i is 30 rows, the P value can be taken as approximately 80% according to the formula. At this time, when the number of identical signs in Di2-Di1 is greater than 80%, and the number of identical signs in Di2-Di3 is greater than 80%, the second column is determined to be a column with fixed-pattern noise; when it is less than or equal to 80%, it is determined that there is no fixed-pattern noise.

[0049] In conjunction with the above, in this embodiment of the invention, determining whether a reference column has fixed-pattern noise based on a first noise signal, a second noise signal, and a third noise signal includes: subtracting the first noise signal and the second noise signal to obtain a first difference value, wherein the number of first differences is multiple, and the number of first differences is the number of rows in the reference column that have a first reference pixel; subtracting the first noise signal and the third noise signal to obtain a second difference value, wherein the number of second differences is multiple, and the number of second differences is the number of rows in the reference column that have a first reference pixel; determining a first number of rows in the first difference and the second difference that have the same sign, and determining a second number of rows in the first difference and the second difference that have the same sign; and determining that the reference column has fixed-pattern noise when the first number and the second number satisfy a preset condition.

[0050] The difference between the first noise signal and the second noise signal is used to obtain the first difference value, which is Di2-Di1. The difference between the first noise signal and the third noise signal is used to obtain the second difference value, which is Di2-Di3. The total number of rows of the reference column is i.

[0051] The number of rows in the first and second differences where both signs are positive can be called the first quantity, and the number of rows in the first and second differences where both signs are negative can be called the second quantity.

[0052] Then, when the first quantity and the second quantity meet the preset conditions, it is determined that the reference column has fixed pattern noise, including: determining a first proportion value of the first quantity occupying the number of reference pixel rows, and determining a second proportion value of the second quantity occupying the number of reference pixel rows; when the first proportion value and the second proportion value are greater than the preset threshold, it is determined that the reference column has fixed pattern noise.

[0053] The preset threshold is determined based on the number of reference pixel rows, and the preset threshold can be represented by P.

[0054] The preset threshold can be determined based on the row number i of the black pixel row 22.

[0055] In this embodiment of the invention, the P value can be adjusted and set according to the row number i of the black pixel row 22.

[0056] The smaller the value of i, that is, the fewer rows of black pixels (row 22), the less accurate the value of P becomes. Therefore, in this embodiment of the invention, to ensure accuracy, the value of i can be 30 or higher. Before the judgment, depending on the accuracy of the analog-to-digital converter, a matrix data that does not affect the number of positive and negative signs can be added, along with the original pixel data of the black row, to separate the number of 0s (Di2-Di1=0) into an equal number of positive and negative signs, thereby eliminating the error caused by too many zero values. Alternatively, the influence of the number of zero values ​​can also be eliminated by adjusting the value of P.

[0057] In this embodiment of the invention, the above operation is performed on each column in the image to determine which columns have fixed pattern noise. The first column and the last column only use the data of the first column minus the second column, and the last column minus the last second column to participate in the determination. The position of the column with fixed pattern noise is saved and the column with fixed pattern noise is used as the target column.

[0058] S103: Perform fixed-pattern noise elimination processing on the valid pixels in the target column indicated by the position of the reference column in the pixel array.

[0059] In the specific execution process of this embodiment of the invention, the average value of the first difference and the second difference is determined, and the average value is used as the parameter value of the reference pixel.

[0060] Specifically, the fixed-pattern noise elimination process is performed on the effective pixels in the target column indicated by the position of the reference column in the pixel array, including: determining the parameter values ​​of the effective pixels in the target column and the parameter values ​​of the reference pixels; subtracting the parameter values ​​of the effective pixels from the parameter values ​​of the reference pixels, and updating the parameter values ​​of the corresponding effective pixels with the result of the subtraction, so as to eliminate the fixed-pattern noise.

[0061] For example, based on the above description, in columns where fixed-pattern noise is determined to exist, when Di2-Di1 and Di2-Di3 are judged to be positive, the average value of Di2-Di1 and Di2-Di3 can be considered as the fixed-pattern noise SNPN. The result of removing fixed-pattern noise is obtained by subtracting the parameter value of the effective pixel from the SNPN. When Di2-Di1 and Di2-Di3 are judged to be negative, the absolute value of the average value of Di2-Di1 and Di2-Di3 can be considered as the fixed-pattern noise SNPN. The result of removing fixed-pattern noise is obtained by adding the parameter value of the effective pixel to the SNPN. The result of subtraction is used to update the parameter value of the corresponding effective pixel to eliminate fixed-pattern noise, thus completing the fixed-pattern noise removal operation for one frame. This noise removal operation is repeated for each frame of the image, or, depending on the actual situation, to effectively save computation time, the judgment can be performed once every N frames, without any limitation.

[0062] In this embodiment, by obtaining the reference column where the reference pixel with preset characteristics is located in the pixel array, the position of the reference column with fixed pattern noise is determined from the reference column, and the fixed pattern noise of the effective pixels in the target column indicated by the position of the reference column is eliminated. This can eliminate noise only for the column pixels with fixed pattern noise, avoid affecting the column pixels without fixed pattern noise, improve the noise elimination effect, and ensure the image presentation effect.

[0063] Figure 5This is a schematic diagram of the image noise processing device proposed in one embodiment of the present invention. See also... Figure 5 The image noise processing apparatus 500 includes:

[0064] The acquisition module 501 is used to acquire the reference column where the reference pixel with preset characteristics is located in the pixel array;

[0065] The first determining module 502 is used to determine the position of the reference column containing fixed pattern noise from the reference column;

[0066] The processing module 503 is used to perform fixed-pattern noise elimination processing on the effective pixels in the target column indicated by the position of the reference column in the pixel array.

[0067] Alternatively, in some embodiments, see Figure 6 The processing module 503 includes:

[0068] The determination submodule 5031 is used to determine the parameter values ​​of the valid pixels and the parameter values ​​of the reference pixels in the target column;

[0069] The update submodule 5032 is used to calculate the difference between the parameter value of the effective pixel and the parameter value of the reference pixel, and then update the parameter value of the corresponding effective pixel using the result of the difference.

[0070] Optionally, in some embodiments, the first determining module 502 is specifically used for:

[0071] For each reference pixel in the reference column, a first noise signal of the first reference pixel is determined, and a second noise signal of a second reference pixel in the preceding column of the reference column and a third noise signal of a third reference pixel in the following column of the reference column are determined.

[0072] The presence of fixed-pattern noise in the reference column is determined based on the first noise signal, the second noise signal, and the third noise signal.

[0073] When it is determined that there is fixed pattern noise in the reference column, the position of the reference column is determined.

[0074] Optionally, in some embodiments, the first determining module 502 is further specifically used for:

[0075] The first noise signal and the second noise signal are subtracted to obtain a first difference value. There are multiple first differences, and the number of first differences is the number of rows with the first reference pixel in the reference column.

[0076] The difference between the first noise signal and the third noise signal is used to obtain a second difference value. There are multiple second differences, and the number of second differences is the number of rows in the reference column that have the first reference pixel.

[0077] Determine the first number of rows with the same sign in the first difference and the second difference, and determine the second number of rows with the same sign in the first difference and the second difference;

[0078] When the first quantity and the second quantity meet the preset conditions, it is determined that there is fixed pattern noise in the reference column.

[0079] Optionally, in some embodiments, the first determining module 502 is further configured to:

[0080] Determine a first proportion value of the first quantity occupying the number of reference pixel rows, and determine a second proportion value of the second quantity occupying the number of reference pixel rows;

[0081] When both the first and second ratio values ​​are greater than a preset threshold, it is determined that the reference column has fixed pattern noise.

[0082] Alternatively, in some embodiments, see Figure 6 It also includes:

[0083] The second determining module 504 is used to determine a preset threshold based on the number of rows of the reference pixels. Optionally, in some embodiments, see [link to relevant documentation]. Figure 6 It also includes:

[0084] The third determining module 505 is used to determine the average of the first difference and the second difference, and use the average as the parameter value of the reference pixel.

[0085] Optionally, in some embodiments, the reference pixel with preset characteristics in the acquisition module 501 is a pixel that cannot sense light.

[0086] It should be noted that the aforementioned Figures 1-4 The explanation of the image noise processing method in the embodiments also applies to the image noise processing device 500 of this embodiment, and its implementation principle is similar, so it will not be repeated here.

[0087] In this embodiment, by obtaining the reference column where the reference pixel with preset characteristics is located in the pixel array, the position of the reference column with fixed pattern noise is determined from the reference column, and the fixed pattern noise of the effective pixels in the target column indicated by the position of the reference column is eliminated. This can eliminate noise only for the column pixels with fixed pattern noise, avoid affecting the column pixels without fixed pattern noise, improve the noise elimination effect, and ensure the image presentation effect.

[0088] Figure 7 This is a schematic diagram of the structure of an image sensor proposed in one embodiment of the present invention.

[0089] See Figure 7 The image sensor 700 includes:

[0090] The image noise processing apparatus 500 of the above embodiment.

[0091] In this embodiment, by obtaining the reference column where the reference pixel with preset characteristics is located in the pixel array, the position of the reference column with fixed pattern noise is determined from the reference column, and the fixed pattern noise of the effective pixels in the target column indicated by the position of the reference column is eliminated. This can eliminate noise only for the column pixels with fixed pattern noise, avoid affecting the column pixels without fixed pattern noise, improve the noise elimination effect, and ensure the image presentation effect.

[0092] This embodiment also provides a computer-readable storage medium storing a computer program thereon, characterized in that the program, when executed by a processor, implements the image noise processing method described above.

[0093] It should be noted that in the description of this invention, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0094] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0095] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0096] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0097] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0098] The storage media mentioned above can be read-only memory, disk, or optical disk, etc.

[0099] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0100] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. An image noise processing method, characterized in that, Includes the following steps: Obtain the reference column in the pixel array where a reference pixel with a preset feature is located, wherein the reference pixel with the preset feature is a pixel that cannot sense light. For each reference pixel in the reference column, a first noise signal of the first reference pixel is determined, and a second noise signal of a second reference pixel in the preceding column of the reference column and a third noise signal of a third reference pixel in the following column of the reference column are determined. Based on the first noise signal, the second noise signal, and the third noise signal, determine whether the reference column has fixed pattern noise; Determining whether the reference column contains the fixed-pattern noise based on the first noise signal, the second noise signal, and the third noise signal includes: The first noise signal and the second noise signal are subtracted to obtain a first difference value. There are multiple first differences, and the number of first differences is the number of rows with the first reference pixel in the reference column. The difference between the first noise signal and the third noise signal is used to obtain a second difference value. There are multiple second differences, and the number of second differences is the number of rows in the reference column that have the first reference pixel. Calculate a first ratio of the number of first differences with all positive signs to the number of reference pixel rows, and a second ratio of the number of second differences with all positive signs to the number of reference pixel rows; When both the first ratio value and the second ratio value are greater than a preset threshold, it is determined that the reference column contains the fixed pattern noise; When it is determined that the reference column contains the fixed-pattern noise, the position of the reference column is determined; Fixed-pattern noise is eliminated for the effective pixels in the target column indicated by the position of the reference column in the pixel array.

2. The image noise processing method as described in claim 1, characterized in that, The fixed-pattern noise removal process for the effective pixels in the target column indicated by the position of the reference column in the pixel array includes: Determine the parameter values ​​of the valid pixels in the target column and the parameter values ​​of the reference pixels; The parameter value of the effective pixel is subtracted from the parameter value of the reference pixel, and the result of the subtraction is used to update the parameter value of the corresponding effective pixel.

3. The image noise processing method as described in claim 1, characterized in that, Also includes: The preset threshold is determined based on the number of reference pixel rows.

4. The image noise processing method as described in claim 3, characterized in that, Also includes: The average value of the first difference and the second difference is determined, and the average value is used as the parameter value of the reference pixel.

5. An image noise processing apparatus, characterized in that, The device includes: The acquisition module is used to acquire the reference column in the pixel array where a reference pixel with a preset feature is located. The reference pixel with the preset feature is a pixel that cannot sense light. The first determining module is configured to determine, for each first reference pixel in the reference column, a first noise signal of the first reference pixel, a second noise signal of the second reference pixel in the preceding column of the reference column, and a third noise signal of the third reference pixel in the following column of the reference column. Based on the first noise signal, the second noise signal, and the third noise signal, determine whether the reference column has fixed pattern noise; When it is determined that the reference column contains the fixed-pattern noise, the position of the reference column is determined; The first determining module is further specifically used for: The first noise signal and the second noise signal are subtracted to obtain a first difference value. There are multiple first differences, and the number of first differences is the number of rows with the first reference pixel in the reference column. The difference between the first noise signal and the third noise signal is used to obtain a second difference value. There are multiple second differences, and the number of second differences is the number of rows in the reference column that have the first reference pixel. Calculate a first ratio of the number of first differences with all positive signs to the number of reference pixel rows, and a second ratio of the number of second differences with all positive signs to the number of reference pixel rows; When both the first ratio value and the second ratio value are greater than a preset threshold, it is determined that the reference column contains the fixed pattern noise; The processing module is used to perform fixed-pattern noise elimination processing on the effective pixels in the target column indicated by the position of the reference column in the pixel array.

6. The image noise processing apparatus as described in claim 5, characterized in that, The processing module includes: A determination submodule is used to determine the parameter values ​​of the valid pixels in the target column and the parameter values ​​of the reference pixels; The update submodule is used to calculate the difference between the parameter value of the effective pixel and the parameter value of the reference pixel, and update the parameter value of the corresponding effective pixel using the result of the difference.

7. The image noise processing apparatus as described in claim 5, characterized in that, Also includes: The second determining module is used to determine the preset threshold based on the number of rows of reference pixels.

8. The image noise processing apparatus as described in claim 7, characterized in that, Also includes: The third determining module is used to determine the average value of the first difference and the second difference, and use the average value as the parameter value of the reference pixel.

9. An image sensor, characterized in that, include: The image noise processing apparatus as described in any one of claims 5-8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the image noise processing method as described in any one of claims 1-4.

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