Image denoising method through combination of adaptive nonlocal samples and low rank

Abstract

The invention discloses an image denoising method through combination of adaptive nonlocal samples and low rank. Firstly an image is transformed to a logarithmic domain by using logarithmic transformation, and a multiplicative noise model is transformed into an additive noise model; the image is partitioned and grouping is performed according to the similarity so as to obtain image groups having similar blocks; then low-tank approximation processing is performed on the image groups so as to obtain an initial estimation value; then adaptive nonlocal sample model processing is performed on the initial estimation value so as to obtain a logarithmic domain recovery result; and finally the logarithmic domain image is restored to a real number domain by using exponential transformation and corrected so as to obtain a final denoised image. The experiment result indicates that the method has great robustness for the multiplicative noise, and the great peak signal-to-noise ratio and the structural similarity can be obtained and the visual quality of the image can be greatly improved for the image having the multiplicative noise.

Description

technical field [0001] The invention relates to the technical field of digital image processing, in particular to an image denoising method combining adaptive non-local samples and low rank. Background technique [0002] The development history of image denoising technology began in the 1950s. Due to the technical defects and external interference in image acquisition and transmission, it is necessary to seek new technologies to make up for these shortcomings and improve image quality. With the rapid development of science and technology, image processing technology has received extensive attention and achieved pioneering achievements in the application fields of people's life, astronomy, biomedicine, VR technology, artificial intelligence, public security, justice, culture and art. The quality of the image will be directly related to the subsequent processing of the image. In the past ten years, many researchers have been looking for various ideas to process images, and ho...

AI Technical Summary

Problems solved by technology

Although the above method has achieved good results in image denoising, it has certain shortcomings in maintaining edges and contours.

[0004] Due to the certain redundant information and similarity structure in the natural image, the image contains similar block matrices, which are theoretically of low rank, but under the interference of noise, the low rank of the image is destroyed

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