Non-local Image Denoising Algorithm Based On Sparse Representation And Low Rank

In the era of large Internet data,the image has become an indispensable part of the exchange of information as an effective carrier of information data transfer and sharing.However,In the process of image transmission,the image signal often subject to noise interference,this interference will have serious impact on image visual effects and image post-processing.Therefore,it is very important to reduce the noise interference of the image.Although there are a large number of image denoising algorithms have been proposed,the research of this problem still needs to be deep investigated.In recent years,there are a great breakthrough in the image denoising based on image self-similarity and the introduction of sparse representation and low rank(low rank can be regarded as matrix form of sparse representation)theory,it becomes the current research focus.This thesis is based on image self-similarity,Analyzing the current excellent sparse representation and low rank non-local image denoising algorithm,optimizing and improving it.The special works in concrete are mainly as follows:(1)Analyze the traditional image denoising and classical non-local image denoising theory and method.Based on the experiment,research on non-local image denoising.Finally,verified the advantage of non-local algorithm in image denoising.(2)The sparse representation of the relevant theory is introduced,and the NCSR non-local image denoising algorithm based on sparse representation is analyzed emphatically.On the basis of clear shortcomings that the brightness and the structure information of the image without considering the Euclidean distance in the NCSR algorithm,improved the distance definition of the self-similarity measure,that is,by introducing the similarity of the structure(SSIM)takes into account the structure and brightness of the image.The related experiments show that the improved algorithm not only improves the PSNR and SSIM,but also improves the visual effect.(3)Introduce the low rank correlation theory,and analyzes emphatically the SAIST non-local image denoising algorithm based on low rank and improve it.a)Considering the noise interference caused by the interference of similar blocks,the DCT pre-filtering method is introduced,which effectively solves the interference of noise at the time of polymerization.b)As the singular value threshold is affected due to the sample mean is not considered for the correlation of the imageand,propose the weighted average of the samples with the correlation between the image blocks is taken into account,which makes the singular value threshold be more adaptable.The related experiments show that the noise image based on the improved algorithm has a certain improvement in objective and subjective evaluation.