Improved threshold function-based wavelet transformation image denoising method

Abstract

The invention relates to an improved threshold function-based wavelet transformation image denoising method which comprises the following steps of: firstly selecting a base wavelet, determining the number of wavelet decomposition layers and carrying out multi-scale wavelet transformation on noise-containing images; respectively determining a threshold for each layer of detail coefficient; carrying out threshold treatment by using improved threshold functions; and finally carrying out wavelet reconstruction on low-frequency coefficients and threshold treated high-frequency coefficients to obtain the denoised images. According to the denoising method provided by the invention, the defect of discontinuity of the hard threshold functions is overcome and the constant deviation in the soft threshold functions is decreased at the same time. By using the denoising method, the signals and the noises can be distinguished effectively, the image information edges are protected when the noises are removed, the peak signal to noise ratio of the images is improved, and better image denoising effect is achieved.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a wavelet transform image denoising method based on an improved threshold function. Background technique [0002] Due to the influence of imaging equipment and external environment interference, digital images inevitably contain various noises. In order to obtain the real information of objective things contained in the image more effectively, image denoising has always been a very important and popular research direction. So far, a variety of denoising methods have been developed. The easiest one is based on the general concentration of noise energy. In the high frequency, while the image spectrum is distributed in a limited interval, the low-pass filtering method is used for denoising, such as sliding average window filter, Wiener filter and so on. It is worth mentioning that in the past ten years, wavelet theory has developed rapidly, and its application to the field of image ...

AI Technical Summary

Problems solved by technology

The disadvantage is that in the hard threshold processing unit, the oscillation caused by the discontinuity of the hard threshold function may still exist

In 2013, Xing Guoquan et al. published a wavelet image denoising method based on a new threshold function, which overcomes the oscillation caused by discontinuity in the image reconstruction of the hard threshold method and eliminates the fixed deviation in the soft threshold method. The details of the image are richer, but the disadvantage is that the deviation is completely reduced to zero, and the original signal itself is composed of real signals and noise signals, which may make the original signal larger due to the influence of noise, so the deviation is in most cases Both exist, and a better way is to gradually reduce rather than completely eliminate the deviation

Images