Gaussian-pulse hybrid image denoising method

Abstract

The invention discloses a Gaussian-pulse hybrid image denoising method. The method comprises the following steps of: detecting a position of a random pulse noise on a target image, filtering the target image to obtained a filtered image, and recording the position of the random pulse noise by using a probability matrix; decomposing the filtered image into a plurality of image blocks, finding corresponding image sub-block iteration tensor forms on the target image, and establishing corresponding tensor low rank-sparse decomposition models; solving an optimization problem in each tensor low rank-sparse decomposition model, and realizing decomposition iterative optimization by adoption of an alternating direction method until convergence is carried out; obtaining a low rank tensor of each tensor low rank-sparse decomposition model and unfolding the low rank tensors in a matrix form, obtaining a denoising result of each image sub-block, and solving an average value of overlapped regions, so as to obtain a final denoising result. By implementing the method disclosed by the invention, the local and non-local statistical characteristics of the images are effectively fused, the problem that the detection precision of self-adaptive median filters is not enough is overcome, and the Gaussian-pulse hybrid noise can be filtered to realize a denoising effect with a higher signal-to-noise ratio.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a Gaussian-pulse mixed image noise removal method. Background technique [0002] During the acquisition and transmission of image signals, the quality is often degraded due to the interference of various external noises, which seriously affects the subsequent processing of images, such as edge detection, target recognition, feature extraction, image segmentation, etc. Therefore, image denoising has become an image The most basic and important link in the processing process, and has attracted widespread attention. However, most of the existing image denoising methods are oriented towards zero-mean Gaussian noise, such as traditional anisotropic diffusion, nonlinear diffusion, bilateral filtering, non-local mean and their related denoising methods. In fact, in practical applications, image noise is diverse. Some scholars believe that the noise generated in the image imagi...

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Problems solved by technology

[0005] The purpose of the embodiment of the present invention is to provide a Gaussian-pulse mixed image noise removal method, which can face noise points between 0-255, and overcome the problem of insufficient accu

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