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

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.
Owner:SHANGHAI INTEGRATED CIRCUIT RES & DEV CENT
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