Wavelet image denoising process based on adaptive sliding window adjacent region threshold

Abstract

The invention relates to a method for denoising wavelet images chosen on the basis of the neighbor data of a self-adapting sliding window. The method comprises the following steps: step 1, decomposing a noisy image into sub-bands by wavelet transformation processing; step 2, processing the wavelet coefficient of each sub-band separately according to the following steps: 1), carrying out threshold judgment of the center wavelet coefficient of each neighbor centering on each wavelet coefficient in each sub-band, comparing the correlativity coefficient Theta of each neighbor of the coefficient, and setting a sliding window, in which the maximal correlativity coefficient Theta is located, as the window for threshold processing; and 2), calculating the Bayes adaptive threshold value of the threshold processing window chosen in 1) so as to obtain a scaling factor; and scaling the wavelet coefficient in the center of the window according to the scaling factor; and step 3, reconstructing the wavelet coefficient so as to obtain the filtered image after processing each sub-band of the wavelet with adaptive sliding window neighbor wavelet process. The method achieves a higher peak value signal-to-noise ratio, thereby having a better protection effect for image edges.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a wavelet image noise reduction method. Background technique [0002] At present, there are many methods of wavelet image denoising, such as soft threshold and hard threshold denoising methods. Among them, the NeighCoeff and NeighBlock methods place the wavelet coefficients to be processed in a square window composed of surrounding coefficients, and the processing of the wavelet coefficients is determined by all the wavelet coefficients in the window; while the NeighShrink method uses all wavelet coefficients in the neighborhood window The size of the sum of squares determines the processing of the wavelet coefficients in the center of the window. Although the NeighShrink method is superior to ordinary soft and hard threshold methods in preserving image details, the size of the sliding window is only determined empirically when using the NeighShrink method f...

AI Technical Summary

Problems solved by technology

Adaptive threshold can solve the problem of uncertain window area to a certain extent, but it cannot well control the amount of data and effective information in the window, which are the fundamental basis for denoising

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