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.