CN111340726A - Image auxiliary denoising method based on supervised machine learning - Google Patents
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Abstract
The invention discloses an image auxiliary denoising method based on supervised machine learning, which adopts a non-subsampled contourlet transform method to convert a noise-containing image into a multi-scale and multi-directional transform sub-band in a Shearlet domain so as to divide a low-frequency sub-band factor without noise and a high-frequency sub-band factor with noise; the method comprises the steps of classifying factors needing to reserve edges and textures and to-be-denoised factors needing to be denoised in high-frequency sub-band factors through a support vector machine algorithm of a least square mode under supervised machine learning, finally removing noise through a soft threshold method to obtain a final auxiliary denoised image, and therefore the method has good denoising capacity, effectively removes noise parts, screens data needing to be denoised in the image layer by layer, ensures image details, saves running time and has high efficiency in the field of image processing.
Description
Technical Field
The invention relates to the field of image signal processing, in particular to an image auxiliary denoising method based on supervised machine learning.
Background
Images are affected by noise, blurring, and the like during recording, transmission, processing, and synthesis, resulting in degradation of image quality. Therefore, in the image processing flow, denoising is the core content of image processing. The noise part and the signal detail of the noise image are dispersed in a high-frequency area and are not easy to distinguish. However, the traditional image-assisted denoising method has the problems of poor denoising effect, less image detail information, long time consumption and the like. Therefore, finding a way to effectively denoise and ensure the image details remains the focus of current academic research.
Disclosure of Invention
Aiming at the defects in the prior art, the image auxiliary denoising method based on the supervised machine learning solves the problems of poor denoising effect, low detail restoration degree and long operation time in the traditional method.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that: an image auxiliary denoising method based on supervised machine learning comprises the following steps:
s1, performing non-subsampled contourlet transformation on the image containing the noise to obtain a high-frequency sub-band factor and a low-frequency sub-band factor;
s2, constructing a least square support vector machine through a least square linear system by using a quadratic programming method, and classifying the high-frequency sub-band factors to obtain edge texture factors and to-be-denoised factors;
s3, carrying out denoising operation on the factor to be denoised by a soft threshold method to obtain a denoising factor;
and S4, splicing the denoising factor, the edge texture factor and the low-frequency subband factor in the Shearlet domain according to coordinates, and performing non-downsampling Shearlet inverse transformation to obtain an auxiliary denoising image.
Further, the step S1 includes the following steps:
s11, carrying out non-downsampling pyramid decomposition on the image containing the noise to obtain multi-scale features;
and S12, performing frequency plane segmentation and directional decomposition on the multi-scale features through a non-downsampling direction filtering device group to obtain high-frequency subband factors and low-frequency subband factors.
Further, the step S3 includes the following steps:
s31, estimating the preliminary noise standard deviation sigma according to the high-frequency sub-band factor by the following formula:
wherein, yijFor a factor value to be denoised of an image containing noise in the ith layer scale and the jth direction of a Shearlet domain, the Median () is a Median function;
s32, according to the factor to be denoised and the preliminary noise standard deviation sigma, estimating a threshold coefficient sigma through the following formulasth：
Wherein σyIn order to be the standard deviation of the noise,
is the noise standard deviation sigma under the current target scale KyThe value range of the front target scale K is in a closed interval [1, N ]]N is the maximum scale, and Max () is the maximum function;
s33, judgment
Whether or not it is greater than a threshold coefficient sigmathIf yes, go to step S24, otherwise, go to step S25;
s34, according to the ideal threshold value ToptCarrying out image-assisted denoising on the factor to be denoised through a soft threshold function to obtain a denoising factor;
s35, according to Bayes lower threshold TLBAnd carrying out image-assisted denoising on the factor to be denoised through a soft threshold function to obtain a denoising factor.
Further, the step S34 is the ideal threshold ToptThe expression of (a) is:
Topt＝argmin(r(T)) (4)
wherein r () is the mean square error function of the denoising factor and the true value of the factor without being affected by noise, and argmin (r (T)) represents the computer iteration process of the value of the threshold T when the r () function is minimized.
Further, the step S34 is based on bayesian lower threshold TLBThe expression of (a) is:
the invention has the beneficial effects that: adopting a non-downsampling contourlet transformation method to convert the noise-containing image into a multi-scale multidirectional transformation sub-band in a Shearlet domain, so as to divide a low-frequency sub-band factor without noise and a high-frequency sub-band factor with noise; the method comprises the steps of classifying factors needing to reserve edges and textures and to-be-denoised factors needing to be denoised in high-frequency sub-band factors through a support vector machine algorithm of a least square mode under supervised machine learning, finally removing noise through a soft threshold method to obtain a final auxiliary denoised image, and therefore the method has good denoising capacity, effectively removes noise parts, screens data needing to be denoised in the image layer by layer, ensures image details, saves running time and has high efficiency in the field of image processing.
Drawings
Fig. 1 is a schematic flow chart of an image-assisted denoising method based on supervised machine learning.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
As shown in fig. 1, in an embodiment of the present invention, an image-assisted denoising method based on supervised machine learning includes the following steps:
s1, performing non-subsampled contourlet transformation on the image containing the noise to obtain a high-frequency sub-band factor and a low-frequency sub-band factor;
s2, constructing a least square support vector machine through a least square linear system by using a quadratic programming method, and classifying the high-frequency sub-band factors to obtain edge texture factors and to-be-denoised factors;
s3, carrying out denoising operation on the factor to be denoised by a soft threshold method to obtain a denoising factor;
and S4, splicing the denoising factor, the edge texture factor and the low-frequency subband factor in the Shearlet domain according to coordinates, and performing non-downsampling Shearlet inverse transformation to obtain an auxiliary denoising image.
Step S1 includes the following steps:
s11, carrying out non-downsampling pyramid decomposition on the image containing the noise to obtain multi-scale features;
and S12, performing frequency plane segmentation and directional decomposition on the multi-scale features through a non-downsampling direction filtering device group to obtain high-frequency subband factors and low-frequency subband factors.
Step S3 includes the following steps:
s31, estimating the preliminary noise standard deviation sigma according to the high-frequency sub-band factor by the following formula:
wherein, yijFor a factor value to be denoised of an image containing noise in the ith layer scale and the jth direction of a Shearlet domain, the Median () is a Median function;
s32, according to the factor to be denoised and the preliminary noise standard deviation sigma, estimating a threshold coefficient sigma through the following formulasth：
Wherein σyIn order to be the standard deviation of the noise,
is the noise standard deviation sigma under the current target scale KyThe value range of the front target scale K is in a closed interval [1, N ]]N is the maximum scale, and Max () is the maximum function;
s33, judgment
Whether or not it is greater than a threshold coefficient sigmathIf yes, go to step S24, otherwise, go to step S25;
s34, according to the ideal threshold value ToptCarrying out image-assisted denoising on the factor to be denoised through a soft threshold function to obtain a denoising factor;
s35, according to Bayes lower threshold TLBAnd carrying out image-assisted denoising on the factor to be denoised through a soft threshold function to obtain a denoising factor.
Step S34 ideal threshold ToptThe expression of (a) is:
Topt＝argmin(r(T)) (4)
wherein r () is the mean square error function of the denoising factor and the true value of the factor without being affected by noise, and argmin (r (T)) represents the computer iteration process of the value of the threshold T when the r () function is minimized.
Step S34 Bayesian lower threshold TLBThe expression of (a) is:
the method adopts a non-downsampling contourlet transform method to convert a noise-containing image into a multi-scale and multi-direction transform sub-band in a Shearlet domain, so as to divide a low-frequency sub-band factor without noise and a high-frequency sub-band factor with noise; the method comprises the steps of classifying factors needing to reserve edges and textures and to-be-denoised factors needing to be denoised in high-frequency sub-band factors through a support vector machine algorithm of a least square mode under supervised machine learning, finally removing noise through a soft threshold method to obtain a final auxiliary denoised image, and therefore the method has good denoising capacity, effectively removes noise parts, screens data needing to be denoised in the image layer by layer, ensures image details, saves running time and has high efficiency in the field of image processing.
Claims (5)
1. An image auxiliary denoising method based on supervised machine learning is characterized by comprising the following steps:
s1, performing non-subsampled contourlet transformation on the image containing the noise to obtain a high-frequency sub-band factor and a low-frequency sub-band factor;
s2, constructing a least square support vector machine through a least square linear system by using a quadratic programming method, and classifying the high-frequency sub-band factors to obtain edge texture factors and to-be-denoised factors;
s3, carrying out denoising operation on the factor to be denoised by a soft threshold method to obtain a denoising factor;
and S4, splicing the denoising factor, the edge texture factor and the low-frequency subband factor in the Shearlet domain according to coordinates, and performing non-downsampling Shearlet inverse transformation to obtain an auxiliary denoising image.
2. The supervised machine learning-based image-assisted denoising method of claim 1, wherein the step S1 comprises the steps of:
s11, carrying out non-downsampling pyramid decomposition on the image containing the noise to obtain multi-scale features;
and S12, performing frequency plane segmentation and directional decomposition on the multi-scale features through a non-downsampling direction filtering device group to obtain high-frequency subband factors and low-frequency subband factors.
3. The supervised machine learning-based image-assisted denoising method of claim 1, wherein the step S3 comprises the steps of:
s31, estimating the preliminary noise standard deviation sigma according to the high-frequency sub-band factor by the following formula:
wherein, yijFor a factor value to be denoised of an image containing noise in the jth direction of the ith layer of a Shearlet domain, the Median () is a Median function;
s32, according to the factor to be denoised and the preliminary noise standard deviation sigma, estimating a threshold coefficient sigma through the following formulasth：
Wherein σyIn order to be the standard deviation of the noise,
is the noise standard deviation sigma under the current target scale KyThe value range of the front target scale K is in a closed interval [1, N ]]N is the maximum scale, and Max () is the maximum function;
s33, judgment
Whether or not it is greater than a threshold coefficient sigmathIf yes, go to step S24, otherwise, go to step S25;
s34, according to the ideal threshold value ToptCarrying out image-assisted denoising on the factor to be denoised through a soft threshold function to obtain a denoising factor;
s35, according to Bayes lower threshold TLBAnd carrying out image-assisted denoising on the factor to be denoised through a soft threshold function to obtain a denoising factor.
4. The supervised machine learning-based image-assisted denoising method of claim 3, wherein the step S34 is performed by using an ideal threshold ToptThe expression of (a) is:
Topt＝argmin(r(T)) (4)
wherein r () is the mean square error function of the denoising factor and the true value of the factor without being affected by noise, and argmin (r (T)) represents the computer iteration process of the value of the threshold T when the r () function is minimized.
5. The supervised machine learning-based image-assisted denoising method of claim 3, wherein the step S34 is based on Bayesian lower threshold TLBThe expression of (a) is:
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