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In traditional inpainting method for object removal, the SSD (Sum of Squared Differences) is always used to measure the degree of similarity between exemplar patch and target patch. Although the matching rule is simple, there is a risk that the target patch is replaced by an unsuitable exemplar patch, which leads to the mismatch error. Even worse, the error may be constantly accumulated along with the process progresses, finally some unexpected objects will be introduced into target region, and the restored image cannot meet the requirements of human vision. In view of these problems, we propose an inpainting method based on adaptive two-round search strategy. Firstly, we define the DBP (Differences Between Patches) between target patch and exemplar patch, and use it to measure the degree of difference between the two patches. Then, based on SSD and DBP, we adaptively judge whether there is a mismatch error. If the mismatch error occurs, the two-round search strategy is implemented. We define a new matching rule and use it to research the exemplar patch. Finally, we use the exemplar patch to restore the target patch. Experimental results demonstrate the effectiveness of our method. It can effectively prevent the occurrence of mismatch error and error accumulation, improve the restoration effect. INDEX TERMS Image inpainting, object removal, two-round search strategy, mismatch error. I. INTRODUCTION As one of the most important branches of image processing and pattern recognition, image inpainting has attracted more and more researchers' attention recently [1], [2]. Its basic idea is to use the effective information in the undamaged regions to estimate and fill the damaged regions according to certain rules, making the restored image more natural, and making the person who is not familiar with the original image cannot notice the restoration traces [3]. At present, image inpainting technology is playing an increasingly important role in many fields [4], such as restoration of old photos and precious historical literature materials, protection of cultural relics [5], film and television special effect production, robot vision, and so on [6], [7]. Up to now, according to the basic idea, existing inpainting methods can be divided into three categories [8]: the PDE-based (Partial Differential Equation) method, the The associate editor coordinating the review of this manuscript and approving it for publication was Wei Liu.

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In the inpainting method for object removal, SSD (Sum of Squared Differences) is commonly used to measure the degree of similarity between the exemplar patch and the target patch, which has a very important impact on the restoration results. Although the matching rule is relatively simple, it is likely to lead to the occurrence of mismatch error. Even worse, the error may be accumulated along with the process continues. Finally some unexpected objects may be introduced into the target region, making the result unable to meet the requirements of visual consistency. In view of these problems, we propose an inpainting method for object removal based on difference degree constraint. Firstly, we define the MSD (Mean of Squared Differences) and use it to measure the degree of differences between corresponding pixels at known positions in the target patch and the exemplar patch. Secondly, we define the SMD (Square of Mean Differences) and use it to measure the degree of differences between the pixels at known positions in the target patch and the pixels at unknown positions in the exemplar patch. Thirdly, based on MSD and SMD, we define a new matching rule and use it to find the most similar exemplar patch in the source region. Finally, we use the exemplar patch to restore the target patch. Experimental results show that the proposed method can effectively prevent the occurrence of mismatch error and improve the restoration effect.

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