Natural image noise removal method based on dual redundant dictionary learning

A dictionary learning, natural image technology, applied in the field of image processing, can solve the problem of rough error control method, loss of texture details, inability to effectively approximate the edge and detail information of the original image, etc., to achieve the effect of fine error control and noise removal

Active Publication Date: 2011-05-25
XIDIAN UNIV
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Problems solved by technology

Its shortcoming is that the atoms of the DCT dictionary are fixed, which cannot effectively approach the edge and detail information of the original i

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  • Natural image noise removal method based on dual redundant dictionary learning
  • Natural image noise removal method based on dual redundant dictionary learning
  • Natural image noise removal method based on dual redundant dictionary learning

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Embodiment Construction

[0035] refer to figure 1 , the implementation steps of the present invention are as follows:

[0036] Step 1: Construct a multi-scale redundant stationary wavelet dictionary R.

[0037] First, the Haar wavelet function is selected and translated accordingly to obtain a multi-scale redundant stationary wavelet dictionary R; the image Y to be denoised is expanded under the multi-scale redundant stationary wavelet dictionary R, ​​and the number of decomposition layers is set to r, then the expansion coefficient scale-by-scale Divide into N=3r+1 blocks, and obtain the coefficient β=[β 1 , β 2 ,...,β N ], j=1, 2...N, set the multi-scale coefficient component β on the jth block j in another redundant dictionary D j The following has a sparse representation, which satisfies the following formula: Y=X+n=R*β+n=R*D*A+n, where Y is the image to be denoised, X is the clear image, n is the noise, and β is The sparse representation coefficients of Y under the multi-scale redundant sta...

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Abstract

The invention discloses a natural image noise removal method based on dual redundant dictionary learning, aiming at mainly solving the problem that texture details are easy to lose in the existing natural image noise removal method. The natural image noise removal method based on dual redundant dictionary learning comprises the following steps: (1) inputting an image Y to be subjected to noise removal; (2) unfolding the image Y to be subjected to noise removal under a multi-scale redundant stable wavelet dictionary R to obtain coefficient components betaj of the image Y on different scales, wherein j is 1, 2,... and N; (3) initializing each variable to make a dictionary Dj be a redundant dictionary DCT, and updating atoms and corresponding coefficient matrixes of the dictionary Dj by adopting the KSVD algorithm; (4) calculating the estimation value of the noise removal results of all the coefficient components betaj; and (5) carrying out multi-scale redundant reverse transformation on all the coefficient components betaj subjected to noise removal to obtain the noise removal result of the image Y to be subjected to noise removal. Compared with the existing classical noise removal method, the natural image noise removal method based on dual redundant dictionary learning can better keep the texture detail information in the image to be subjected to noise removal and can be used for natural image noise removal.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a denoising processing of natural images, which can be used in the fields of image processing, pattern recognition and biomedicine. Background technique [0002] The goal of denoising is to preserve the feature information of the image, such as texture, edges and point objects, while removing noise. The noise of natural images has an additive background, and denoising can be carried out from both the spatial domain and the transform domain. Typical spatial domain filtering methods include Lee filtering, non-local mean filtering, and redundant dictionary image denoising. in: [0003] Lee filtering adopts the mean value in the homogeneous area, and adopts the local filtering strategy for the fast-changing points. One of its disadvantages is that it cannot effectively remove the noise around the edge or the texture of the over-smoothed image; the second disadvantage is: O...

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Application Information

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IPC IPC(8): G06T5/00G06K9/40
Inventor 杨淑媛焦李成卫美绒张月圆胡在林缑水平王爽侯彪
Owner XIDIAN UNIV
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