Research On Image Restoration Technology Based On Sparse Representation

Recovering a clear image from its degraded version has a wide range of applications in medical imaging,surveillance video,etc.However,due to the complexity and diversity of the causes of image contaminated by noise,there is no practical restoration method.Image restoration remains to be a hot and difficult topic in computer vision.Image prior knowledge plays a key role in image restoration,sparsity prior and nonlocal self-similar prior are the most commonly used prior knowledge in image restoration.In practice,the joint use of sparsity prior,nonlocal self-similar prior and other prior knowledge can achieve better results of image restoration.Therefore,this thesis focuses on combining sparsity prior,nonlocal self-similar prior with other prior knowledge of image restoration,and mainly includes two parts.First,Gaussian mixture models is used to learn image prior knowledge from natural clean images,then,singular value decomposition is exploited to learn dictionary from the learned Gaussian mixture models,and then based on the sparse representation,a new image restoration algorithm is designed by using sparsity prior,nonlocal self-similar prior and Gaussian prior of natural images,which achieves competitive denoising results.Second,image denoising problem is regarded as a low rank matrix completion problem,and then the weighted nuclear norm minimization is used to deal with the low rank matrix completion problem.Additionally,in order to reconstruct more image details,sparsity representation technique is introduced into the algorithm,image denoising model is established by jointly utilizing the sparse representation and low rank matrix approximation,and then the alternate iteration method is utilized to solve the model.The algorithm realizes the goal of image denoising by using sparsity prior,nonlocal self-similar prior and low rank prior,and this method is comparable with the state-of-the-art denoising methods.Experimental results show that combining sparsity prior,nonlocal self-similar prior with other proper prior knowledge can achieve better restoration results.