Methods, apparatus, computer equipment and storage media for strip noise removal

Abstract

This application relates to a method, apparatus, computer device, and storage medium for stripe noise removal. The method includes: acquiring a preprocessed image; processing the preprocessed image to form a low-frequency image and a high-frequency image; performing wavelet domain Fourier transform denoising processing on the low-frequency image; summing the result of the low-frequency image processing with the high-frequency image to remove broadband noise, obtaining a first processed image; performing Gaussian low-pass filtering on the first processed image; performing row mean difference on the images before and after Gaussian low-pass filtering and superimposing the row mean difference result pixel-by-pixel with the corresponding rows of the first processed image to remove narrow stripe noise, obtaining a second processed image; performing median filtering on the second processed image to remove particle noise in the image, and outputting a third processed image. This application can effectively remove both wide stripe noise and narrow stripe noise.

Description

Technical Field

[0001] This application relates to the field of strip noise removal technology, and in particular to a strip noise removal method, apparatus, computer equipment, and storage medium. Background Technology

[0002] Currently, there is a lot of research on image stripe noise removal methods. Among them, the combination of wavelet transform and Fourier transform and the moment matching method are two commonly used methods for stripe noise removal. These two algorithms will be introduced below.

[0003] The algorithm for removing strip noise based on a combination of wavelet transform and Fourier transform first performs multi-scale wavelet decomposition on the image. Since the image is a horizontal stripe, the horizontal wavelet sub-band containing strip noise is subjected to Fourier transform, and band-stop filtering is applied to the Fourier frequency domain to eliminate strip noise. Then, inverse Fourier transform is performed. Finally, the vertical wavelet sub-band, diagonal wavelet sub-band, low-frequency wavelet sub-band, and processed horizontal wavelet sub-band after wavelet decomposition are reconstructed. The reconstruction result is the processed image.

[0004] The main idea of ​​the moment matching algorithm is to select the mean and standard deviation of one column of pixels as a reference, and then perform a linear transformation on the mean and standard deviation of the other columns accordingly. The transformed columns of pixels will then have the same mean and standard deviation as the reference column. The transformation formula is as follows:

[0005]

[0006] Where: DN i It is the pixel grayscale value in the original image; μ i It is the mean of column i of the original pixels; σ i It is the standard deviation of column i of the original pixels; μ ref It is the mean of the reference column; σ ref The standard deviation of the reference column; The value is the transformed pixel value.

[0007] The above-mentioned methods for removing strip noise have the following drawbacks: 1. While the wavelet and Fourier transform combined algorithm can effectively remove horizontal strip noise, it loses much of the high-frequency details outside the stripes in the image. 2. The wavelet and Fourier transform combined algorithm is more suitable for removing broadband noise, but its effect on removing narrowband noise is poor. 3. The image strip noise removal algorithm based on moment matching is based on the assumption of statistical consistency. When the obtained image has a large grayscale range or the strip noise is wide, it is easy to generate new stripes, resulting in poor noise removal. Moreover, it is more effective at removing narrowband noise, but cannot effectively remove broadband noise. Summary of the Invention

[0008] Therefore, it is necessary to provide a method, apparatus, computer device, and storage medium for strip noise removal that can not only remove wide strip noise but also effectively remove narrow strip noise, in order to address the above-mentioned technical problems.

[0009] On the one hand, a method for strip noise removal is provided, the method comprising:

[0010] Obtain the preprocessed image, and process the preprocessed image to form low-frequency and high-frequency images;

[0011] The low-frequency image is subjected to wavelet domain Fourier transform denoising processing, and the result of the low-frequency image processing is summed with the high-frequency image to remove broadband noise, thus obtaining the first processed image.

[0012] The first processed image is subjected to Gaussian low-pass filtering. The images before and after Gaussian low-pass filtering are then subjected to row mean difference, and the row mean difference result is superimposed pixel by pixel with the corresponding row of the first processed image to remove narrow strip noise, thus obtaining the second processed image.

[0013] The second processed image is subjected to median filtering to remove particle noise from the image, and a third processed image with wide and narrow stripe noise removed is output.

[0014] In one embodiment, the step of performing Gaussian low-pass filtering on the preprocessed image to form a low-frequency image and a high-frequency image includes:

[0015] The preprocessed image is subjected to Gaussian low-pass filtering to form a low-frequency image;

[0016] The high-frequency image is obtained by subtracting the preprocessed image from the low-frequency image.

[0017] In one embodiment, in the step of performing Gaussian low-pass filtering on the preprocessed image to form a low-frequency image, a 5*5 filter template with a mean square error σ of 1 is used during the Gaussian low-pass filtering process.

[0018] In one embodiment, the step of performing wavelet domain Fourier transform denoising on the low-frequency image, and summing the processed low-frequency image with the high-frequency image to remove broadband noise, to obtain the first processed image includes:

[0019] The low-frequency image is decomposed into multiple horizontal wavelet sub-bands.

[0020] Fourier transforms are performed on the decomposed horizontal wavelet subbands respectively, and high-frequency noise is suppressed by band-stop filtering to form a Fourier frequency domain image.

[0021] Perform an inverse Fourier transform on the Fourier domain image to remove strip noise from each horizontal wavelet sub-band;

[0022] The processed horizontal wavelet subbands are reconstructed using wavelet remodeling to obtain the first processed image.

[0023] In one embodiment, the step of performing wavelet decomposition on the low-frequency image to form multiple decomposed horizontal wavelet subbands includes:

[0024] The wavelet decomposition of the low-frequency image is performed at level 5, and the resulting multiple horizontal wavelet sub-bands are three-level wavelet sub-bands.

[0025] In one embodiment, the step of performing Gaussian low-pass filtering on the first processed image, performing row mean difference on the images before and after Gaussian low-pass filtering, and superimposing the row mean difference result with the corresponding row of the first processed image pixel by pixel to remove narrow strip noise, to obtain the second processed image includes:

[0026] The first processed image is subjected to Gaussian low-pass filtering to obtain an intermediate image;

[0027] The first processed image and the intermediate image are processed line by line. The average value of each line of the first processed image is subtracted from the average value of each line of the intermediate image to obtain the line difference value of each line.

[0028] The second processed image is obtained by adding the row difference to the first processed image pixel by pixel.

[0029] In one embodiment, in the step of performing Gaussian low-pass filtering on the first processed image to obtain an intermediate image, a filter template of size 15*15 is used when performing Gaussian low-pass filtering, and the mean square error σ is 1.

[0030] On the other hand, a strip noise removal apparatus is provided, the apparatus comprising:

[0031] The image preprocessing module is used to acquire preprocessed images and process them to form low-frequency and high-frequency images.

[0032] The wide stripe removal module is used to perform wavelet domain Fourier transform noise removal on the low-frequency image, and sum the result of the low-frequency image processing with the high-frequency image to remove broadband noise, thereby obtaining the first processed image.

[0033] The narrow stripe removal module is used to perform Gaussian low-pass filtering on the first processed image, perform row mean difference on the images before and after Gaussian low-pass filtering on the first processed image, and superimpose the row mean difference result with the corresponding row of the first processed image pixel by pixel to remove narrow stripe noise and obtain the second processed image.

[0034] The median filtering module is used to perform median filtering on the second processed image to remove particle noise from the image and output a third processed image with wide and narrow stripe noise removed.

[0035] In another aspect, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0036] Obtain the preprocessed image, and process the preprocessed image to form low-frequency and high-frequency images;

[0037] The low-frequency image is subjected to wavelet domain Fourier transform denoising processing, and the result of the low-frequency image processing is summed with the high-frequency image to remove broadband noise, thus obtaining the first processed image.

[0038] The first processed image is subjected to Gaussian low-pass filtering. The images before and after Gaussian low-pass filtering are then subjected to row mean difference, and the row mean difference result is superimposed pixel by pixel with the corresponding row of the first processed image to remove narrow strip noise, thus obtaining the second processed image.

[0039] The second processed image is subjected to median filtering to remove particle noise from the image, and a third processed image with wide and narrow stripe noise removed is output.

[0040] In another aspect, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0041] Obtain the preprocessed image, and process the preprocessed image to form low-frequency and high-frequency images;

[0042] The low-frequency image is subjected to wavelet domain Fourier transform denoising processing, and the result of the low-frequency image processing is summed with the high-frequency image to remove broadband noise, thus obtaining the first processed image.

[0043] The first processed image is subjected to Gaussian low-pass filtering. The images before and after Gaussian low-pass filtering are then subjected to row mean difference, and the row mean difference result is superimposed pixel by pixel with the corresponding row of the first processed image to remove narrow strip noise, thus obtaining the second processed image.

[0044] The second processed image is subjected to median filtering to remove particle noise from the image, and a third processed image with wide and narrow stripe noise removed is output.

[0045] The above-mentioned stripe noise removal method, apparatus, computer equipment, and storage medium can remove not only wide stripe noise but also narrow stripe noise effectively. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a flowchart illustrating a stripe noise removal method in one embodiment;

[0048] Figure 2 This is a schematic diagram of a stripe noise removal method in one embodiment;

[0049] Figure 3 This is a flowchart illustrating the steps of performing Gaussian low-pass filtering on a preprocessed image to form a low-frequency image and a high-frequency image in one embodiment.

[0050] Figure 4 In one embodiment, a flowchart of the first image processing step is shown, in which the low-frequency image is denoised by wavelet domain Fourier transform, and the result of the low-frequency image processing is summed with the high-frequency image to remove broadband noise.

[0051] Figure 5 This is a schematic diagram illustrating the principle of wavelet decomposition of the low-frequency image in one embodiment;

[0052] Figure 6 In one embodiment, the first processed image is subjected to Gaussian low-pass filtering. The images before and after Gaussian low-pass filtering are subjected to row mean difference, and the row mean difference result is superimposed pixel by pixel with the corresponding row of the first processed image to remove narrow strip noise, thus obtaining a flowchart of the second processing image step.

[0053] Figure 7 This is a structural block diagram of a strip noise removal device in one embodiment;

[0054] Figure 8 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0056] In one embodiment, such as Figure 1 , Figure 2 As shown, a method for strip noise removal is provided, including the following steps:

[0057] Step S1: Obtain the preprocessed image, and process the preprocessed image to form a low-frequency image and a high-frequency image;

[0058] Step S2: Perform wavelet domain Fourier transform noise reduction on the low-frequency image, and sum the result of the low-frequency image processing with the high-frequency image to remove broadband noise, thereby obtaining the first processed image.

[0059] Step S3: Perform Gaussian low-pass filtering on the first processed image, perform row mean difference on the images before and after Gaussian low-pass filtering, and superimpose the row mean difference result with the corresponding row of the first processed image pixel by pixel to remove narrow strip noise and obtain the second processed image.

[0060] Step S4: Perform median filtering on the second processed image to remove particle noise from the image, and output a third processed image with wide and narrow stripe noise removed.

[0061] like Figure 3 As shown, in this embodiment, the step of performing Gaussian low-pass filtering on the preprocessed image to form low-frequency and high-frequency images includes:

[0062] Step S11: Perform Gaussian low-pass filtering on the preprocessed image to form a low-frequency image;

[0063] Step S12: Subtract the low-frequency image from the preprocessed image to obtain the high-frequency image.

[0064] In this embodiment, in the step of performing Gaussian low-pass filtering on the preprocessed image to form a low-frequency image, a 5*5 filter template with a mean square error σ of 1 is used during the Gaussian low-pass filtering process.

[0065] like Figure 4 As shown, in this embodiment, the step of performing wavelet domain Fourier transform denoising on the low-frequency image, and summing the processed low-frequency image with the high-frequency image to remove broadband noise, to obtain the first processed image includes:

[0066] Step S21: Perform wavelet decomposition on the low-frequency image to form multiple horizontal wavelet sub-bands after decomposition;

[0067] Step S22: Perform Fourier transform on the decomposed multiple horizontal wavelet subbands respectively, and suppress high-frequency noise by band-stop filtering to form a Fourier frequency domain image.

[0068] Step S23: Perform an inverse Fourier transform on the Fourier domain image to remove strip noise in each horizontal wavelet sub-band;

[0069] Step S24: Perform wavelet reconstruction on the processed horizontal wavelet subband to obtain the first processed image.

[0070] In the wavelet and Fourier transform combined processing method of this invention, the image is first subjected to low-pass filtering and frequency division, and only the low-frequency part is processed by wavelet and Fourier transform combined processing, while the high-frequency part is not processed. The processed low-frequency part and high-frequency part are added together to achieve broadband noise removal of the image.

[0071] In this embodiment, the step of performing wavelet decomposition on the low-frequency image to form multiple horizontal wavelet subbands after decomposition includes:

[0072] The wavelet decomposition of the low-frequency image is performed at level 5, and the resulting multiple horizontal wavelet sub-bands are three-level wavelet sub-bands.

[0073] Wavelet transform, also known as wavelet decomposition, decomposes a two-dimensional image at different scales to obtain approximate and detail values ​​of the original information. Specifically, these are approximate components, horizontal detail components, vertical detail components, and diagonal detail components. After each wavelet transform, the image is divided into a quarter of a sub-band region.

[0074] like Figure 5 As shown, the LL band is a thumbnail of the image content, preserving the original image content information. The LH band contains high-frequency edge information in the horizontal direction, the HL band contains high-frequency edge information in the vertical direction, and the HH band contains high-frequency edge information in the diagonal direction, reflecting the comprehensive transformation of image grayscale in the horizontal and vertical directions.

[0075] Fourier transforms were performed on the decomposed horizontal wavelet subbands HL1, HL2, HL3, HL4, and HL5. Due to the presence of horizontal stripe noise, high-frequency noise in the vertical direction appeared in the Fourier frequency domain. This high-frequency noise was suppressed by band-stop filtering. Finally, an inverse Fourier transform was performed on the processed Fourier domain image, thus essentially removing the stripe noise in each horizontal wavelet subband.

[0076] Let the processed horizontal wavelet subbands be HL1', HL2', HL3', HL4', and HL5'. First, perform wavelet reconstruction on the 5th-level wavelet subbands HL5', LH5, HH5, and LL5, and use the reconstructed image as LL4' in the 4th-level wavelet subband. Then, perform wavelet reconstruction on the 4th-level wavelet subbands HL4', LH4, HH4, and LL4', and use the reconstructed image as LL3' in the 3rd-level wavelet subband. And so on, until the reconstruction of each wavelet subband is completed.

[0077] like Figure 6 As shown, in this embodiment, the step of performing Gaussian low-pass filtering on the first processed image, performing row mean difference on the images before and after Gaussian low-pass filtering, and superimposing the row mean difference result with the corresponding row of the first processed image pixel by pixel to remove narrow strip noise and obtain the second processed image includes:

[0078] Step S31: Perform Gaussian low-pass filtering on the first processed image to obtain an intermediate image;

[0079] Step S32: Process the first processed image and the intermediate image line by line, subtract the average value of each line of the first processed image from the average value of each line of the intermediate image to obtain the line difference value of each line.

[0080] Step S33: The first processed image is obtained by adding the row difference to each pixel of the first processed image.

[0081] The narrowband noise removal method in this invention involves first performing a low-pass filter to blur the image, resulting in a blurred image with virtually no stripe noise. The mean values ​​of corresponding rows in the unfiltered and filtered images are subtracted to obtain the row gain. Finally, the original image is subtracted from the row gain to achieve the goal of narrowband noise removal.

[0082] In this embodiment, in the step of performing Gaussian low-pass filtering on the first processed image to obtain an intermediate image, a filter template of size 15*15 is used when performing Gaussian low-pass filtering, and the mean square error σ is 1.

[0083] This invention removes wide-strip noise and narrow-strip noise using different methods. Wide-strip noise is removed using a transform domain approach, which combines wavelet and Fourier transform. Narrow-strip noise is removed by subtracting the row difference between the images before and after filtering from the corresponding row of the original image.

[0084] The strip noise removal algorithm proposed in this invention can remove not only wide strip noise, but also narrow strip noise effectively.

[0085] 1. The method for removing stripe noise from microscopic images proposed in this invention is mainly divided into two parts. First, the image is processed by a combination of wavelet and Fourier transform to remove wide stripe noise. Then, the image is processed by the difference of the row mean values ​​of the images before and after filtering, and the difference of the row mean values ​​is superimposed on the corresponding rows of the original image pixel by pixel to achieve the effect of removing narrow stripe noise.

[0086] 2. The wavelet and Fourier transform combined method proposed in this invention adopts a frequency division processing approach, which only performs wavelet and Fourier transform combined noise reduction processing on the low frequency part, while the high frequency is not processed. Finally, the high frequency and low frequency are added together to obtain the processed image. Not only can broadband noise be effectively removed, but image details can also be well preserved.

[0087] 3. The narrowband noise removal method proposed in this invention does not change the grayscale distribution of image pixels, and can also remove narrowband noise in the image.

[0088] It should be understood that, although Figure 1 , Figure 3 , Figure 4 , Figure 6 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 , Figure 3 , Figure 4 , Figure 6 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0089] In one embodiment, such as Figure 7 As shown, a strip noise removal device 10 is provided, including: an image preprocessing module 1, a wide strip removal module 2, a narrow strip removal module 3, and a median filtering module 4.

[0090] The image preprocessing module 1 is used to acquire a preprocessed image and process the preprocessed image to form a low-frequency image and a high-frequency image.

[0091] The wide stripe removal module 2 is used to perform wavelet domain Fourier transform noise removal on the low-frequency image, and sum the result of the low-frequency image processing with the high-frequency image to remove broadband noise, thereby obtaining the first processed image.

[0092] The narrow stripe removal module 3 is used to perform Gaussian low-pass filtering on the first processed image, perform row mean difference on the images before and after Gaussian low-pass filtering, and superimpose the row mean difference result with the corresponding row of the first processed image pixel by pixel to remove narrow stripe noise and obtain the second processed image.

[0093] The median filtering module 4 is used to perform median filtering on the second processed image to remove particle noise in the image and output a third processed image with wide stripe noise and narrow stripe noise removed.

[0094] In this embodiment, the step of performing Gaussian low-pass filtering on the preprocessed image to form low-frequency and high-frequency images includes:

[0095] The preprocessed image is subjected to Gaussian low-pass filtering to form a low-frequency image;

[0096] The high-frequency image is obtained by subtracting the preprocessed image from the low-frequency image.

[0097] In this embodiment, in the step of performing Gaussian low-pass filtering on the preprocessed image to form a low-frequency image, a 5*5 filter template with a mean square error σ of 1 is used during the Gaussian low-pass filtering process.

[0098] In this embodiment, the step of performing wavelet domain Fourier transform denoising on the low-frequency image, and summing the processed low-frequency image with the high-frequency image to remove broadband noise, to obtain the first processed image includes:

[0099] The low-frequency image is decomposed into multiple horizontal wavelet sub-bands.

[0100] Fourier transforms are performed on the decomposed horizontal wavelet subbands respectively, and high-frequency noise is suppressed by band-stop filtering to form a Fourier frequency domain image.

[0101] Perform an inverse Fourier transform on the Fourier domain image to remove strip noise from each horizontal wavelet sub-band;

[0102] The processed horizontal wavelet subbands are reconstructed using wavelet remodeling to obtain the first processed image.

[0103] In this embodiment, the step of performing wavelet decomposition on the low-frequency image to form multiple horizontal wavelet subbands after decomposition includes:

[0104] The wavelet decomposition of the low-frequency image is performed at level 5, and the resulting multiple horizontal wavelet sub-bands are three-level wavelet sub-bands.

[0105] In this embodiment, the step of performing Gaussian low-pass filtering on the first processed image, performing row mean difference on the images before and after Gaussian low-pass filtering, and superimposing the row mean difference result with the corresponding row of the first processed image pixel by pixel to remove narrow strip noise and obtain the second processed image includes:

[0106] The first processed image is subjected to Gaussian low-pass filtering to obtain an intermediate image;

[0107] The first processed image and the intermediate image are processed line by line. The average value of each line of the first processed image is subtracted from the average value of each line of the intermediate image to obtain the line difference value of each line.

[0108] The second processed image is obtained by adding the row difference to the first processed image pixel by pixel.

[0109] In this embodiment, in the step of performing Gaussian low-pass filtering on the first processed image to obtain an intermediate image, a filter template of size 15*15 is used when performing Gaussian low-pass filtering, and the mean square error σ is 1.

[0110] The technical effect of the above-mentioned strip noise removal device.

[0111] Specific limitations regarding the strip noise removal device can be found in the limitations of the strip noise removal method described above, and will not be repeated here. Each module in the aforementioned strip noise removal device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0112] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 8 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and the database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores stripe noise removal data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements a stripe noise removal method.

[0113] Those skilled in the art will understand that Figure 8The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0114] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0115] Obtain the preprocessed image, and process the preprocessed image to form low-frequency and high-frequency images;

[0116] The low-frequency image is subjected to wavelet domain Fourier transform denoising processing, and the result of the low-frequency image processing is summed with the high-frequency image to remove broadband noise, thus obtaining the first processed image.

[0117] The first processed image is subjected to Gaussian low-pass filtering. The images before and after Gaussian low-pass filtering are then subjected to row mean difference, and the row mean difference result is superimposed pixel by pixel with the corresponding row of the first processed image to remove narrow strip noise, thus obtaining the second processed image.

[0118] The second processed image is subjected to median filtering to remove particle noise from the image, and a third processed image with wide and narrow stripe noise removed is output.

[0119] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0120] The step of performing Gaussian low-pass filtering on the preprocessed image to form low-frequency and high-frequency images includes:

[0121] The preprocessed image is subjected to Gaussian low-pass filtering to form a low-frequency image;

[0122] The high-frequency image is obtained by subtracting the preprocessed image from the low-frequency image.

[0123] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0124] In the step of performing Gaussian low-pass filtering on the preprocessed image to form a low-frequency image, a 5*5 filter template with a mean square error σ of 1 is used during the Gaussian low-pass filtering process.

[0125] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0126] The step of performing wavelet domain Fourier transform denoising on the low-frequency image, and summing the processed low-frequency image with the high-frequency image to remove broadband noise, to obtain the first processed image includes:

[0127] The low-frequency image is decomposed into multiple horizontal wavelet sub-bands.

[0128] Fourier transforms are performed on the decomposed horizontal wavelet subbands respectively, and high-frequency noise is suppressed by band-stop filtering to form a Fourier frequency domain image.

[0129] Perform an inverse Fourier transform on the Fourier domain image to remove strip noise from each horizontal wavelet sub-band;

[0130] The processed horizontal wavelet subbands are reconstructed using wavelet remodeling to obtain the first processed image.

[0131] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0132] The step of performing wavelet decomposition on the low-frequency image to form multiple horizontal wavelet subbands includes:

[0133] The wavelet decomposition of the low-frequency image is performed at level 5, and the resulting multiple horizontal wavelet sub-bands are three-level wavelet sub-bands.

[0134] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0135] The step of performing Gaussian low-pass filtering on the first processed image, performing row mean difference on the images before and after Gaussian low-pass filtering, and superimposing the row mean difference result with the corresponding row of the first processed image pixel by pixel to remove narrow strip noise, to obtain the second processed image includes:

[0136] The first processed image is subjected to Gaussian low-pass filtering to obtain an intermediate image;

[0137] The first processed image and the intermediate image are processed line by line. The average value of each line of the first processed image is subtracted from the average value of each line of the intermediate image to obtain the line difference value of each line.

[0138] The second processed image is obtained by adding the row difference to the first processed image pixel by pixel.

[0139] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0140] In the step of performing Gaussian low-pass filtering on the first processed image to obtain an intermediate image, a filter template of size 15*15 is used when performing Gaussian low-pass filtering, and the mean square error σ is 1.

[0141] For specific limitations on the steps a processor takes when executing a computer program, please refer to the limitations on stripe noise removal methods mentioned above, which will not be repeated here.

[0142] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0143] Obtain the preprocessed image, and process the preprocessed image to form low-frequency and high-frequency images;

[0144] The low-frequency image is subjected to wavelet domain Fourier transform denoising processing, and the result of the low-frequency image processing is summed with the high-frequency image to remove broadband noise, thus obtaining the first processed image.

[0145] The first processed image is subjected to Gaussian low-pass filtering. The images before and after Gaussian low-pass filtering are then subjected to row mean difference, and the row mean difference result is superimposed pixel by pixel with the corresponding row of the first processed image to remove narrow strip noise, thus obtaining the second processed image.

[0146] The second processed image is subjected to median filtering to remove particle noise from the image, and a third processed image with wide and narrow stripe noise removed is output.

[0147] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0148] The step of performing Gaussian low-pass filtering on the preprocessed image to form low-frequency and high-frequency images includes:

[0149] The preprocessed image is subjected to Gaussian low-pass filtering to form a low-frequency image;

[0150] The high-frequency image is obtained by subtracting the preprocessed image from the low-frequency image.

[0151] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0152] In the step of performing Gaussian low-pass filtering on the preprocessed image to form a low-frequency image, a 5*5 filter template with a mean square error σ of 1 is used during the Gaussian low-pass filtering process.

[0153] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0154] The step of performing wavelet domain Fourier transform denoising on the low-frequency image, and summing the processed low-frequency image with the high-frequency image to remove broadband noise, to obtain the first processed image includes:

[0155] The low-frequency image is decomposed into multiple horizontal wavelet sub-bands.

[0156] Fourier transforms are performed on the decomposed horizontal wavelet subbands respectively, and high-frequency noise is suppressed by band-stop filtering to form a Fourier frequency domain image.

[0157] Perform an inverse Fourier transform on the Fourier domain image to remove strip noise from each horizontal wavelet sub-band;

[0158] The processed horizontal wavelet subbands are reconstructed using wavelet remodeling to obtain the first processed image.

[0159] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0160] The step of performing wavelet decomposition on the low-frequency image to form multiple horizontal wavelet subbands includes:

[0161] The wavelet decomposition of the low-frequency image is performed at level 5, and the resulting multiple horizontal wavelet sub-bands are three-level wavelet sub-bands.

[0162] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0163] The step of performing Gaussian low-pass filtering on the first processed image, performing row mean difference on the images before and after Gaussian low-pass filtering, and superimposing the row mean difference result with the corresponding row of the first processed image pixel by pixel to remove narrow strip noise, to obtain the second processed image includes:

[0164] The first processed image is subjected to Gaussian low-pass filtering to obtain an intermediate image;

[0165] The first processed image and the intermediate image are processed line by line. The average value of each line of the first processed image is subtracted from the average value of each line of the intermediate image to obtain the line difference value of each line.

[0166] The second processed image is obtained by adding the row difference to the first processed image pixel by pixel.

[0167] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0168] In the step of performing Gaussian low-pass filtering on the first processed image to obtain an intermediate image, a filter template of size 15*15 is used when performing Gaussian low-pass filtering, and the mean square error σ is 1.

[0169] For specific limitations on the implementation steps of a computer program when it is executed by a processor, please refer to the limitations on stripe noise removal methods mentioned above, which will not be repeated here.

[0170] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0171] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0172] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for strip noise removal, characterized in that, include: Obtain the preprocessed image, and process the preprocessed image to form low-frequency and high-frequency images; The low-frequency image is subjected to wavelet domain Fourier transform denoising processing, and the result of the low-frequency image processing is summed with the high-frequency image to remove broadband noise, thus obtaining the first processed image. The first processed image is subjected to Gaussian low-pass filtering. The images before and after Gaussian low-pass filtering are then subjected to row mean difference, and the row mean difference result is superimposed pixel by pixel with the corresponding row of the first processed image to remove narrow strip noise, thus obtaining the second processed image. The second processed image is subjected to median filtering to remove particle noise from the image, and a third processed image with wide and narrow stripe noise removed is output.

2. The strip noise removal method according to claim 1, characterized in that, The step of performing Gaussian low-pass filtering on the preprocessed image to form low-frequency and high-frequency images includes: The preprocessed image is subjected to Gaussian low-pass filtering to form a low-frequency image; The high-frequency image is obtained by subtracting the preprocessed image from the low-frequency image.

3. The strip noise removal method according to claim 2, characterized in that, In the step of performing Gaussian low-pass filtering on the preprocessed image to form a low-frequency image, a 5*5 filter template with a mean square error σ of 1 is used during the Gaussian low-pass filtering process.

4. The strip noise removal method according to claim 1, characterized in that, The step of performing wavelet domain Fourier transform denoising on the low-frequency image, and summing the processed low-frequency image with the high-frequency image to remove broadband noise, to obtain the first processed image includes: The low-frequency image is decomposed into multiple horizontal wavelet sub-bands. Fourier transforms are performed on the decomposed horizontal wavelet subbands respectively, and high-frequency noise is suppressed by band-stop filtering to form a Fourier frequency domain image. Perform an inverse Fourier transform on the Fourier frequency domain image to remove strip noise from each horizontal wavelet sub-band; The processed horizontal wavelet subbands are reconstructed using wavelet remodeling to obtain the first processed image.

5. The strip noise removal method according to claim 1, characterized in that, The step of performing wavelet decomposition on the low-frequency image to form multiple horizontal wavelet subbands includes: The wavelet decomposition of the low-frequency image is performed at level 5, and the resulting multiple horizontal wavelet sub-bands are three-level wavelet sub-bands.

6. The strip noise removal method according to claim 1, characterized in that, The step of performing Gaussian low-pass filtering on the first processed image, performing row mean difference on the images before and after Gaussian low-pass filtering, and superimposing the row mean difference result with the corresponding row of the first processed image pixel by pixel to remove narrow strip noise, to obtain the second processed image includes: The first processed image is subjected to Gaussian low-pass filtering to obtain an intermediate image; The first processed image and the intermediate image are processed line by line. The average value of each line of the first processed image is subtracted from the average value of each line of the intermediate image to obtain the line difference value of each line. The second processed image is obtained by adding the row difference to the first processed image pixel by pixel.

7. The strip noise removal method according to claim 6, characterized in that, In the step of performing Gaussian low-pass filtering on the first processed image to obtain an intermediate image, a filter template of size 15*15 is used when performing Gaussian low-pass filtering, and the mean square error σ is 1.

8. A strip noise removal device, characterized in that, The device includes: The image preprocessing module is used to acquire preprocessed images and process them to form low-frequency and high-frequency images. The wide stripe removal module is used to perform wavelet domain Fourier transform noise removal on the low-frequency image, and sum the result of the low-frequency image processing with the high-frequency image to remove broadband noise, thereby obtaining the first processed image. The narrow stripe removal module is used to perform Gaussian low-pass filtering on the first processed image, perform row mean difference on the images before and after Gaussian low-pass filtering on the first processed image, and superimpose the row mean difference result with the corresponding row of the first processed image pixel by pixel to remove narrow stripe noise and obtain the second processed image. The median filtering module is used to perform median filtering on the second processed image to remove particle noise from the image and output a third processed image with wide and narrow stripe noise removed.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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