Recovery method of low-rank matrix reconstruction with random value impulse noise deletion image

Abstract

The present invention belongs to the field of the computer vision, and the objective of the invention is to realize the recovery of the image with random value impulse noise structural deficiency. The method comprises: combining the matrix reconstruction theory and the sparse expression theory, leading into a dictionary learning model on the basis of the traditional matrix reconstruction model, and therefore solving the problems which cannot be solved in the prior art. The method comprises the following steps: 1) taking the image as a matrix, expressing an original image by using a matrix A, and solving the deletion image recovery problem with random value pulse noise to solve the following optimization equation; 2) training a dictionary [Phi]; 3) converting a sequence to another sequence for solution by using an alternative direction method (ADM), wherein a contraction operator is included; and performing iteration solution to obtain a final result according to the steps. The recovery method of low-rank matrix reconstruction with a random value impulse noise deletion image is mainly applied to the computer image processing.

Description

technical field [0001] The invention belongs to the field of computer vision. In particular, it concerns missing image restoration methods with random-valued impulse noise based on low-rank matrix reconstruction. Background technique [0002] The matrix reconstruction problem is mainly divided into matrix filling and matrix restoration, which has attracted much attention and has strong vitality since it was proposed. Especially in the era of big data, it has become a research hotspot in the fields of mathematics and computers. In recent years, there have been many research results on algorithms to solve the matrix reconstruction problem. Since the rank minimization problem of the matrix is ​​a non-convex optimization problem, the current algorithm mainly uses the iterative singular value decomposition method to approximate the solution of the original model. Such as SVT (singular value threshold) algorithm, APG (accelerated neighbor gradient) algorithm, ALM (augmented Lag...

AI Technical Summary

Problems solved by technology

The technical scheme adopted by the present invention is that low-rank matrix reconstruction has a random value impulse noise missing image recovery method, combines matrix reconstruction theory with sparse representation theory, and introduces a dictionary learning model on the basis of traditional matrix reconstruction models, thereby solving the existing problems. There are problems that technology cannot handle

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