Shearing wave image denoising method based on cutoff window

Abstract

The invention discloses a shearing wave image denoising method based on a cutoff window, which mainly solves the problem of large time complexity of a traditional shearing wave based on a first basis function algorithm and the problems of inflexible frequency band division and poor denoising effect of the traditional shearing wave based on a traditional Laplace pyramid algorithm, and comprises the following steps of: carrying out discrete Fourier transformation on a noisy image and the construction on a rectangular cutoff window or a round cutoff window; obtaining an approximate image througha rectangular central cutoff window or a round central cutoff window, carrying out direction division on the frequency bands of the rectangular cutoff window or the round cutoff window acting on the frequency domain of the noisy image by the shearing wave to obtain a coefficient matrix; afterwards carrying out hard threshold denoising on the coefficient matrix; and then reconstructing the denoised coefficient matrix and the approximate image by combining the rectangular cutoff window or the round cutoff window with the shearing wave to obtain the denoised image. The cutoff window constructed by the invention can obtain more ideal effect on image denoising and can be used for image analysis.

Description

technical field [0001] The invention belongs to the field of image processing and relates to an image denoising method, which can be used for image denoising based on total variation, hard threshold and non-local mean. Background technique [0002] Shear wave analysis is a new multi-scale geometric analysis tool that inherits the advantages of curve waves and square waves. It generates shear wave functions with different characteristics through radial transformations such as scaling, shearing, and translation of basis functions. For C 2 Singular curves or curved high-dimensional signals have optimal properties. For two-dimensional signals, it can not only detect all singular points, but also adaptively track the direction of the singular curve. With the change of the scale parameter, it can accurately describe the singularity characteristics of the function, and realize the description by classical multi-scale analysis. The singularity in high-dimensional signals also estab...

AI Technical Summary

Problems solved by technology

However, the traditional Laplacian pyramid decomposition combined with shear wave, the coefficient matrix obtained by the image is not too sparse, so when the threshold denoising is applied, the edge information of the image cannot be effectively extracted, and the edge information of the image cannot be eliminated. Noise, so that the edge information of the denoised image is not well preserved, which directly leads to a relatively low peak signal-to-noise ratio PSNR of the image, and the denoising effect is not ideal

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