Wavelet image denoising process based on sliding window adjacent region data selection

Abstract

The invention relates to a method for denoising wavelet images chosen on the basis of the neighbor data of a 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 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 carrying out 2) if the maximal correlativity coefficient Theta is higher than an empirical value, or carrying out step 3 directly if the maximal correlativity coefficient Theta is lower than the empirical value; 2), calculating the Bayes adaptive threshold value of the threshold processing window chosen in 1) so as to obtain a scaling factor; and 3), 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 has the advantages of higher peak value signal-to-noise ratio and 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. The NeighShrink method is superior to ordinary soft and hard threshold methods in retaining image details. However, in the denoising process, it is easy to treat edge data as noise, smoothing edge information, and ...

AI Technical Summary

Problems solved by technology

The NeighShrink method is superior to ordinary soft and hard threshold methods in retaining image details. However, in the denoising process, it is easy to treat the edge data as noise, which makes the edge information smooth and blurs the image.

Images