A wavelet denoising method based on a self-adaptive non-local mean value

Abstract

The invention provides a wavelet denoising method based on a self-adaptive non-local mean value. The method comprises the following steps: step S110: carrying out following steps on each color channelcontaining noise images: S111, extracting a low-frequency wavelet component and a high-frequency wavelet component of a noise-containing image under a color channel by adopting a wavelet transform algorithm; S112, calculating neighborhood calibration noise in a high-frequency wavelet component search window according to a constructed edge discrimination operator; S113, according to the domain calibration noise, sequentially calculating the similarity between a reference window and a target neighborhood window in a search window, and determining a weighting coefficient of a target wavelet coefficient based on the similarity; Step S114, according to the weighting coefficient of the target wavelet coefficient, updating the target wavelet coefficient, and based on the updated target wavelet coefficient, obtaining a denoised image containing the noise image under the color channel by using a wavelet inverse transformation algorithm; And S120, synthesizing the de-noised image under each color channel to obtain a de-noised image containing the noise image. The method improves wavelet denoising.

Description

technical field [0001] The invention relates to image signal processing, in particular to a wavelet denoising method based on adaptive non-local means. Background technique [0002] In recent years, the advancement of science and technology has promoted the popularization of various digital products. As the most important data display method in digital products, digital images directly reflect the performance of related products. Therefore, the development of digital image processing related technologies has naturally become Research hotspots and priorities in this field. [0003] Digital image processing refers to the process of converting image signals into digital signals and processing them with computers. The purpose is to process image information to meet human visual psychology or specific application needs. Image processing technology has received widespread attention and made many pioneering achievements in many fields, such as aerospace, biomedical engineering, in...

AI Technical Summary

Problems solved by technology

The modulus maximum method and coefficient correlation method are not widely used because of the need to integrate wavelet coefficients at different scales to complete feature classification and achieve denoising, the algorithm complexity is high, and the denoising effect is general; the coefficient threshold method is the most widely used because of its simplicity and efficiency , but the accuracy of threshold selection is limited by the prior knowledge of wavelet coefficients. On the other hand, the threshold method has limited ability to distinguish noise coefficients from signal coefficients, especially the global threshold denoising method, which leads to image details while denoising. Loss of information, resulting in image blur effects

[0007] It can be seen that the existing wavelet image denoising is still unable to effectively protect the details of the image while denoising.

Images