Image Inpainting Method Based On Irregular Patch

The highlight and difficult point of the current research in terms of image completion is to complete the image of a large damaged region with complex structure and rich texture information.Traditional exemplar-based completion algorithms mainly adapt the regular sample and matching patches to complete the damaged region.During the completion process,the irregular information,especially the local irregular information,of the image structure and texture cannot be fully used.This condition affects the accuracy and efficiency of the algorithm.To solve this problem,an image inpainting method based on irregular patch is proposed in this paper.The specific work is as follows:(1)Image completion method with irregular patch is proposed.Firstly,the structure sparsity is used to distinguish the structure information and texture information of the input image.And the patch priority of pixels on the boundary of the damaged region is calculated on the basis of the structure sparsity and confidence terms.Next,the pixel of the highest priority is selected as the center of the sample patch.If the neighborhood of a sample patch,which is in the complex structure area of the image,contains the known structure information,then this regular patch is dilated to construct its corresponding irregular sample patch by using structure information in its neighborhood.Then,the matching patch is being searched in the known part of the image.If the neighborhood of this matching patch contains a valid structure information,then the matching patch is dilated,and the structure information in its neighborhood is supplemented to construct its corresponding irregular matching patch.Finally,this irregular matching patch is used to complete its corresponding damaged region.In terms of the problem of visual disconnectivity,which is caused by seams during the completion process,the texture information of the image is used for modification in the proposed algorithm.(2)The improved image inpainting method based on irregular patch can overcome some problems such as the patch size and sparsity threshold cannot be adjusted adaptively,the global search strategy takes a large amount of computation and matching function does not take the structural features of image into account.First,the proposed algorithm introduces the double threshold structure sparsity which can calculate the high and low thresholds of structural sparsity for different image features.Moreover,the patch size is adjusted adaptively by double structure sparsity threshold.Then,the traditional search strategy is improved.The global random search and local depth matching are combined to improve the search efficiency.Finally,the calculation formula of the similarity function is optimized.The structural constraints are added to the similarity function and the contribution values of different regions are distinguished.By this way,the matching accuracy can be improved while avoiding the accumulation and propagation of errors.Our approach is compared with five other similar completion algorithms through experimental verification.The results show that the proposed algorithm can achieve a better result in the fineness of the completion as well as the visual connectivity subjectively.Moreover,the PSNR of the proposed algorithm is improved by 0 dB to 4 dB,and the value of SSIM is closer to 1 objectively.The proposed algorithm has a good application in the restoration of damaged natural images and cultural relics with both geometric structure and rich texture as well as the removal of target objects.Thus,the proposed algorithm is generalizable.