Abstract
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 re-search 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.
Highlights
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]
The values of our method are much smaller than that of other methods. It means that when a mismatch error occurs, our method re-searches the exemplar patch through the two-round search strategy, which can effectively prevent the occurrence of mismatch and the accumulation of errors, and prevent unexpected objects from appearing in the restored image, making the restoration image satisfy the requirements of human visual consistency, as shown in Fig. 12 (h)
Summary
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]. Compared with the PDE-based method, this category of method can reduce the restoration time, and prevent the over-smooth phenomenon in restoring the large-scale damaged region, and maintain the integrity and continuity of the texture These methods still have some problems [18], [19], such as unreasonable filling order, mismatch error, error accumulation, greedy search, and so on. Based on the method of Morphological Component Analysis (MCA), Elad et al [23] proposed an inpainting method which can simultaneously restore the overlapping texture layer and cartoon layer For smooth images, these methods can obtain good visual results. Some undesired objects may be introduced into the restored images, making the results unable to meet the requirements of visual consistency In view of these problems, we propose an inpainting method based on adaptive two-round search strategy.
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