A Noise Removal Method Based on New Norm

Abstract

The invention discloses an image noise removal method based on a new norm, which includes performing block matching on a target image, then stacking the matched similar image blocks into a tensor form, and establishing a corresponding principal component analysis model; a The definition of the new norm, and the definition of the new norm is extended from the matrix to the tensor case, and the rank function in the principal component analysis model established in the previous steps is replaced by the new norm, thereby transforming the original NP-hard problem into It is a solvable problem, and an analytical solution to a new optimization problem is proposed; the low-rank tensor obtained by solving the new problem is expanded into a matrix form, and the denoising result of each image sub-block is obtained, and the overlapping area is averaged to obtain the final Denoising results. By implementing the present invention, the improved method effectively fuses the local and non-local statistical characteristics of the image, effectively utilizes the spatial structure information, improves the robustness to noise and singular points, and realizes 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 an image noise removal method based on a new norm. 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. [0003] The current image denoising methods are mainly divided into local methods and non-local methods. A local approach usually involves convolving the image with some kind of kernel. However, because the local method only uses the relationship between the pixel in the area where the current pixel is located and the pixel for denoising, it will easily lose ...

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Problems solved by technology

[0004] However, in the existing non-local image denoising method, the inventors found that there are the following disadvantages: one, when the image block is expanded into a vector, it is easy to lose the spatial distribution information of the image; two, due to the principal component analysis model (matrix rank minimization problem) is a non-deterministic polynomial (NP) hard problem, and when the strong incoherence condition is satisfied, the nuclear norm minimization problem can obtain the solution of the original problem with a high probability

But unfortunately, in practical situations, the non-coherence condition is difficult to be satisfied

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