Research On Patch-based Sparse Representation For Color Image Inpainting

Digital image inpainting technology is technique to fill the damaged area of the image,which can make the repaired image not affect visual sense.Nowadays,the technology is widely used,mainly for the protection of cultural relics,restoration of old photos and removal of subtitles.According to the principle of digital image inpainting,this thesis takes the image inpainting method of sparse representation as the main research content.Firstly,it compares and analyzes several existing algorithms,and improves the inadequacies of the algorithm.Then this thesis discusses the patch-based sparse representation for color image inpainting using the perspectives of dictionary construction,the patch transmission of sparse structure and the selection of matched patches.The main research work and innovations of this thesis are summarized as follows:1.In the process of traditional patch group based image inpainting,it is easy to synthetic the incorrect filling patch which causes incoherence at the edge because of the incorrect matching patches,a regular-weighted sparse representation image inpainting algorithm is proposed.Firstly,the patch matching criterion is defined by the combination of color information with the cosine distance,it is used to obtain matching patches whose structural change trend is more similar to the target patch to form the dictionary.Secondly,in the process of sparse reconstruction,in order to make the whole sparse representation model more capable of filtering matching patch,we consider both the known information and the estimated unknown information to calculate the matching degree between every similar patch and target patch,which is used to add different weights on the sparse coefficients.Finally,the structural sparsity is used to reflect the structural complexity,and the patches of different sizes are adaptively used in different regions in this algorithm.The experimental results show that the peak signal-to-noise ratio(PSNR)of the result is improved about 0.5dB～ 3dB compared with relevant image inpainting algorithms.2.Aiming at the error caused by the propagation strategy based on structural sparsity in the image inpainting process,a new propagation method using neighborhood partition statistics is proposed.Firstly,the neighborhood window is divided into four partitions,and then calculates the similarity between degraded patch and multiple image patches taken from different partitions to distinguish the areas with strongly directional changes of structure.Then,the structural confidence in the priority function is improved to achieve the effect of preferential repair of the edge structure and reduce the texture extension during the inpainting process.Secondly,the search window size is adjusted by confidence factor to improve the efficiency of the inpainting algorithm.Experimentalresults show that the algorithm improves the problem of fracture or texture extension in the process of edge restoration,and not only improves the subjective vision significantly,but also improves the peak signal-to-noise ratio(PSNR)about 1～4dB.