Application Of Inexact Augmented Lagrange Method In Image Processing

As we all know,in this era full of information,image is the main carrier of information,however,in the process of collection,storage and transmission of images,due to the inevitable impact of external factors or machines and equipment,images often appear incomplete and quality decline.So this paper will focus on the image processing denoising,blocking and repair problems,in order to solve these problemsAt present,through the research of most researchers,the image processing problem can be converted into a non-convex optimization problem for processing,while Inexact Augmented Lagrange Method(IALM)plays an extremely important role in solving constrained optimization problems,whether in image,it is also widely used in the fields of signaling,economics and social sciences.Therefore,the rapid development of imprecise augmented Lagrange algorithm not only drives the vigorous development of optimization field,but also plays an important role in image processing.Therefore,mathematical models are established for the three problems in image processing and solved by the IALM.The main innovative work of this paper includes the following three points:1.Aiming at the problem of salt and pepper noise generated in the process of image formation,transmission and processing,an image denoising algorithm combining median filtering,matrix low-rank sparse decomposition and guided filtering is designed.The algorithm firstly blocks the image with overlapping pixels,and uses the IALM to solve the matrix low-rank sparse decomposition model corresponding to the noisy image block to obtain clean image and noisy image.Meanwhile,the median filtering algorithm is used to process the noisy image.The sum of the processed image and the clean image is taken as the guide image,and the noisy image is taken as the input image.Image restoration using guided filtering algorithm.Experimental results show that the proposed algorithm is superior to the comparison algorithm.2.Aiming at the problem of block artifacts generated in JPEG(Joint Photo graphic Experts Group,JPEG)compressed images,an image deblocking algorithm based on IALM and Hybrid Structural Sparsification Error(HSSE)is proposed.Firstly,the internal priori information of the image is used to remove image noise.Secondly,in order to prevent the problem of inaccurate image restoration caused by over-fitting of image data,the external prior information of image is introduced.However,degraded images usually contain damaged structures which lead to the failure to accurately find similar blocks,so it is combined with Gaussian curvature filtering;Finally,the IALM is used to solve the proposed model.Experimental results show that the proposed algorithm is superior to the comparison algorithm in terms of visual quality and evaluation index.3.Aiming at the problem of image blur caused by various degradation factors,a fuzzy image repair algorithm based on the IALM and non-local self-similar priori is designed.Firstly,the fuzzy image is preprocessed by guiding filter.Secondly,a fuzzy image restoration model combining non-local self-similar priori,depth priori and wavelet transform is constructed.Finally,the IALM is used to solve the model.Considering the influence of penalty factor on image restoration,the relative residual adaptive principle is introduced to update penalty factor in depth denoising device.The experimental results show that the proposed algorithm is better than the comparison algorithm in both subjective visual effects and evaluation indexes for restoring the degraded images with superimposed blur and noise.