Research on Image Denoising Algorithm Based on Improved Wavelet Threshold and Non-local Mean Filtering

Abstract

Wavelet threshold denoising and non-local mean denoising are traditional image denoising methods, but wavelet hard threshold denoising will produce pseudo Gibbs phenomenon due to some discontinuous wavelet coefficients after wavelet reconstruction; wavelet soft threshold denoising is in After wavelet reconstruction, the image accuracy will be reduced due to the constant deviation between the approximate wavelet coefficients and the original wavelet coefficients; traditional non-local mean filtering will increase the noise of similar local blocks due to the search for weights with the increase of noise, resulting in low confidence The noise denoising effect of the noise ratio is not good. In view of the above situation, this paper proposes an improved wavelet threshold combined with non-local mean filtering to denoise images. The image denoising effect is better than wavelet threshold denoising or non-local mean denoising.