Bayesian denoising method based on wavelet low frequency

Abstract

The invention discloses a Bayesian denoising method based on wavelet low frequency. The method comprises the following steps of: 1, inputting a natural image to be denoised; 2, selecting pixel blocks to be estimated; 3, selecting a central low-frequency-coefficient block; 4, determining a searching window; 5, selecting a low-frequency-coefficient block; 6, judging whether a constraint condition is satisfied; 7, computing a similarity weight value; 8, judging whether all points in the searching window are searched; 9, computing the recovery values of the pixel blocks to be estimated; 10, judging whether the natural image to be denoised is searched completely; and 11, integrating the recovery values. The similarity weight value is computed by using a wavelet low frequency coefficient. Compared with the conventional denoising method, the Bayesian denoising method has the advantages that: the edge and texture details of the natural image can be well kept and recovered while noise is well smoothened; and the Bayesian denoising method can be applied to denoising processing of the natural image.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a filtering method of natural image processing technology, which uses wavelet low-frequency coefficients to describe the similarity weight in the Bayesian denoising model, and uses Bayesian non-local mean filtering It is a denoising model that can be used to denoise natural images. Background technique [0002] The main purpose of image denoising is to solve the problem of image quality degradation caused by noise interference in actual images. Image quality can be improved by denoising, the signal-to-noise ratio can be increased, and the information carried by the image can be better reflected. Therefore, image denoising technology occupies an important position in many fields. [0003] According to the characteristics of the image and the statistical characteristics of the noise, many image denoising methods have been proposed over the years. The existing filter...

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that the processing process of this method is too complicated, and the accuracy of the feature vector function cannot be guaranteed, and it cannot maintain and restore the edge and texture details of the natural image while smoothing the noise better.

This method also only has an obvious denoising effect on high-noise images, and the effect on low-noise images is general.

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