To inpaint the image with large damaged region and complex structure texture, a new method based on neighborhood reference priority which can not only maintain image character but also improve inpainting speed was proposed, by which the problem of image inpainting was translated into the best sample searching process. Firstly,the structure information of target image was extracted, and the sample region was divided into several sub-regions to reduce the sample size and the search scope. Secondly, in order to solve the problem that Sum of Squares of Deviations (SSD) method ignores the matching of structure information, structure symmetry matching constraint was introduced into matching method, which effectively avoided wrong matches and improves sample matching precision and searching efficiency. Then, priority formulas which highlights the effect of structure was obtained by introducing structure weight and confidence and combining the traditional priority calculation. Finally,the priority of four-neighborhood was got by computing overlapping information between target block and neighborhood blocks patches, according to the reliable reference information provided by four-neighborhood and the improved block matching method, the samples were pruned and the optimal sample was retrieved. The inpainting was completed until all the the optimal samples for all the target blocks were retrieved. The experimental results demonstrate that the proposed method can overcome the problems like texture blurring and structure dislocations and so on, the Peak Signal-to-Noise Ratio (PSNR) of the improved algorithm is increased by 0.5 dB to 1 dB compared with the contrast methods with speeding up inpainting process, the recovered image is much continuous for human vision. Meanwhile, it can effectively recover common damaged images and is more pervasive.

When inpainting the image of large damaged region with complex geometric structure and rich texture, the PatchMatch-based image inpainting algorithm has disadvantages like texture extension and some incorrect sample patches being selected as candidate patches. To solve these problems, a new image inpainting algorithm was proposed for improving accuracy and efficiency. In terms of exact matching of sample patches, an image was preprocessed to obtain priori information of the image, which was used to initialize the constraint of the offset map, while PathMatch algorithm used global random initialization. In the process of pixel patch matching, to improve the matching accuracy of the sample, mean method and angle method were introduced to compute the similarity of different categories of pixel patches. In terms of efficiency, according to the statistical characteristics of similar patches of an image, histogram statistical method was introduced to reduce the labels for inpainting. The proposed algorithm was verified by some instances. The simulation results show that compared with the original PatchMatch algorithm, the Peak Signal-to-Noise Ratio (PSNR) of the proposed algorithm was improved by 0.5dB to 1dB, and the running time was reduced by 5s to 10s, which indicates that the proposed algorithm can effectively improve the accuracy and efficiency of image inpainting.

In order to solve the problem of inpainting missing information in the large damaged region with rich texture information and complex structure information, an image inpainting algorithm for partitioning feature subregions was proposed. Firstly, according to the different features contained in the image, the feature formula was used to extract the features, and the feature subregions were divided by the statistical eigenvalues to improve the speed of image inpainting. Secondly, on the basis of the original Criminisi algorithm, the calculation of priority was improved, and the structural fracture was avoided by increasing the influence of the structural term. Then, the optimal sample patch set was determined by using the target patch and its optimal neighborhood similar patches to constrain the selection of sample patch. Finally, the optimal sample patch was synthesized by using weight assignment method. The experimental results show that, compared with the original Criminisi algorithm, the Peak Signal-to-Noise Ratio (PSNR) of the proposed algorithm is improved by 2-3 dB; compared with the patch priority weight computation algorithm based on sparse representation, the inpainting efficiency of the proposed algorithm is also obviously improved. Therefore, the proposed algorithm is not only suitable for the inpainting of small-scale damaged images, but also has better inpainting effect for large damaged images with rich texture information and complex structure information, and the restored images are more in line with people's visual connectivity.