Curvature variation based wavelet image denoising algorithm

Abstract

The invention relates to a curvature variation based wavelet image denoising algorithm. The curvature variation based wavelet image denoising algorithm is characterized by comprising the steps of 1, algorithm description, namely, performing wavelet transformation for an input image to be denoised, introducing a horizontal set curvature as a correction factor into a variation model, and creating the curvature variation based wavelet image denoising algorithm; 2, algorithm verification, namely, the first item for the curvature variation model is a dispersion item in the image smoothing process, and the second item of the curvature variation model is designed to be the control function of the image structure, to maintain the integral structure of the image; 3, algorithm simulation, namely, performing the simulation algorithm of MATLAB software, and analyzing the timeliness and complexity of the algorithm through the simulation result. With the adoption of the algorithm, the processed image can be clear and close to the original image; the signal to noise of the denoised image is increased by about 15dB by being compared with that of a TV model; the classic wavelet threshold denoising algorithm is increased by about 25dB, and moreover, the definition is greatly improved.

Description

technical field [0001] The invention belongs to the technical field of digital image processing, in particular to a wavelet transform image denoising algorithm based on curvature variation. Background technique [0002] With the deep development and wide application of digital image processing technology, the quality requirements of digital image processing are getting higher and higher. In practical applications, the generation and transmission of digital images will be mixed with some random pulses or other noise interference, which seriously affects the quality of the image. Therefore, before processing the image such as edge detection, contrast enhancement and image segmentation, image denoising It is the primary task of image processing. At present, there are two mainstream methods for image denoising: partial differential equation (Partial Differential Equation, PDE) image denoising and wavelet denoising. [0003] In Rudin L I, Osher S, Fatemi E.Nonlinear Total Varia...

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Problems solved by technology

Although this method can solve the problem of plaque in the image after denoising by the existing three-dimensional block matching denoising algorithm, it is suitable for the preprocessing of natural images, but the method still has the following obvious deficiencies: one is the use of Variance judges the smooth area, and the selection of the judgment parameter δ=6 has great randomness, which affects the accuracy of smooth area judgment and further affects the effect of denoising; the second is step (3) described in each primal sketch line The point is the center, and a 7*7 window is made along the direction of the primal sketch line segment to divide the structural area and the non-structural area. Fourth, although the peak signal-to-noise ratio of the denoising result of this method is better than that of the non-local mean method, compared with the three-dimensional matching block method, the peak signal-to-noise ratio is reduced, indicating that the method Poor stability; Fifth, although the structural similarity SSIM index of this method is better than that of the non-local mean method and the BM3D method, the improvement is very small and the effect is not obvious

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