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Non-local TV model image denoising method based on singular value weight functions

A technology of weight function and singular value, applied in the field of image processing, can solve the problem of noise interference of pixel similarity weight assignment, so as to reduce the interference and improve the effect.

Active Publication Date: 2017-06-20
ZHEJIANG UNIV OF TECH
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Problems solved by technology

[0009] The purpose of the present invention is to overcome the defect that in the current existing image denoising technology based on non-local theory, after the image is disturbed by noise, the pixel value of the image is polluted by noise and changes, and the pixel similarity weight assignment will be disturbed by noise

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[0025] The non-local TV model image denoising method based on singular value weight function of the present invention, concrete steps are as follows:

[0026] (1) First input N 0 ×N 0 The size of the image to be denoised f;

[0027] (2) The relevant parameters of the inventive method are set, including the non-local search window size N 1 ×N 1 , Neighborhood window size N 2 ×N 2 , the parameters h and j of the pixel similarity weight function, the standard deviation σ of the Gaussian kernel, and the split Bregman iterative auxiliary variable b k The initial value of b 0 , smoothing parameter θ and fidelity parameter λ;

[0028] (3) Let M x is the size N centered on the pixel point x∈Ω in the noisy image f input in step (1). 2 ×N 2 The pixel gray value matrix of the image block, Ω is the image space of f, and different pixels correspond to different image blocks. Each image block M x Perform singular value decomposition: M x =U x Λ x V x T . where U x , V x ...

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Abstract

Provided is a non-local TV model image denoising method based on singular value weight functions. The method includes the steps of (1) inputting a noise image; (2) setting the relative parameters of the algorithm, including the non-local search window size N1*N1, neighborhood window size N2*N2, pixel similarity weight function parameters h, j, Gaussian kernel standard deviation sigma, split Bregman iterative auxiliary variable initial value b0, fidelity parameter gamma, and smoothing parameter Theta; (3) obtaining the largest singular value of an image block through a singular value decomposition method; (4) constructing a new pixel similarity weight function based on the largest singular value; (5) establishing a non-local TV model by using the weight function constructed in step (4); (6) solving the non-local TV model established in step (5) by using a split Bregman algorithm; (7) obtaining the denoised image by the split Bregman algorithm value iterative operation; (8) if the iteration satisfies the stop condition, outputting the iterative optimal result image and going to step (9), if the iteration doesn't satisfy the stop condition, returning to step (7) to continue the iteration; and (9) taking the iterative optimal result image in step (8) as the final denoising result image.

Description

[0001] 1. Technical field [0002] The invention belongs to the technical field of image processing, and in particular relates to the field of image denoising for removing additive noise and an image denoising method of an improved non-local TV model. [0003] 2. Background technology [0004] Image denoising aims to reduce the influence of noise on the original useful information by processing the image polluted by noise, and restore the image before being polluted by noise as much as possible. [0005] The non-local TV model (Nonlocal Total Variation, NLTV) proposed by GUY GILBOA and STANLEY OSHER is a non-local operator proposed by GUY GILBOA and STANLEY OSHER (see literature: NONLOCAL OPERATORS WITH APPLICATIONS TO IMAGE PROCESSING.SIAM Multiscale Modeling and Simulation.Vol.7 , No.3, pp.1005–1028) introduced into the Total Variation (TV) model proposed by Rudin-Osher-Fatemi, the NLTV model has the ability to better preserve the image while removing image noise Characteriz...

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Application Information

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IPC IPC(8): G06T5/00
CPCG06T2207/20012G06T5/70
Inventor 金燕蒋文宇万宇赵羿杜伟龙王雪丽
Owner ZHEJIANG UNIV OF TECH
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