Self-adaptive wavelet threshold de-noising method based on neighborhood correlation

Abstract

The invention discloses a self-adaptive wavelet threshold de-noising method based on neighborhood correlation. The method includes the following steps that (1) wavelet transformation is performed on images with noise to obtain wavelet coefficients; (2) according to the wavelet coefficients, self-adaptive threshold functions of each layer of wavelet coefficients are constructed, and wavelet threshold values of an ith decomposition layer are selected; (3) attenuation coefficients are selected by the utilization of a mid-point method, and threshold processing is performed by the adoption of the threshold functions and the wavelet threshold values; (4) wavelet inverse transformation is performed on the wavelet coefficients, corresponding to threshold function processing, of the selected attenuation coefficients to obtain restored original signal estimation values; (5) PSNR values of the original signal estimation values are worked out to obtain an optimal value, an optimal attenuation coefficient under the PSNR optimal value is obtained according to the mid-point method, and the wavelet coefficients corresponding to the threshold function processing are reconstructed, and obtained estimation values are used as final de-noising images. According to the method, the defects of hard threshold and soft threshold de-noising methods are overcome, more accurate wavelet coefficient estimation values are obtained, and the edges of the images are protected.

Description

technical field [0001] The invention belongs to the technical field of image denoising in image preprocessing, in particular to an adaptive wavelet threshold denoising method based on field correlation. Background technique [0002] Noise is an important factor affecting image quality. In the process of image acquisition, transmission and acquisition, image noise will inevitably be introduced due to the interference of imaging equipment or external environment. The addition of noise will bring a lot of trouble to the subsequent processing of the image, so before the image is used, it is necessary to perform denoising processing to improve the signal-to-noise ratio. The purpose of image denoising is to remove most of the noise while protecting the edge information of the image as much as possible to reduce the impact of noise on subsequent processing. Image denoising is of great help to the processing of image engineering, therefore, image denoising has great research value ...

AI Technical Summary

Problems solved by technology

[0010] Although these two commonly used wavelet threshold denoising methods are widely used and the effect is good, they have obvious shortcomings.

The discontinuity in the hard threshold denoising method causes some oscillations in the reconstructed signal; although the soft threshold method is continuous, there is a constant deviation between the estimated signal and the real signal, which will produce some ambiguity

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