An image inpainting method for object removal based on difference degree constraint
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An image inpainting method for object removal based on difference degree constraint Lei Zhang1
· Minhui Chang1
Received: 21 February 2020 / Revised: 11 August 2020 / Accepted: 9 September 2020 / © The Author(s) 2020
Abstract 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. Keywords Image inpainting · Object removal · Exemplar · Difference degree
1 Introduction Image inpainting derives from the restoration of damaged artworks [5]. Its basic idea is to use the undamaged and effective information to restore the damaged regions according to certain rules [11, 30]. Its main purpose is to make the restored image meet the requirements of human vision, so that people who are not familiar with the original image cannot notice the restoration trace [17, 33]. With the rapid development of computer and multimedia technology, the inpainting technology has been widely used in many fields [20], such as Lei Zhang
[email protected] 1
School of Mathematics and Information Technology, Yuncheng University, Yuncheng, China
Multimedia Tools and Applications
scratches restoration of old photos and precious literatures, protection of cultural relics [8, 18], robot vision, film and television special effects production, and so on [3, 37]. At present, the existing inpainting approaches can be classified into three categories. The first is based on the Partial Differential Equation (PDE). Its basic idea is that the missing region is filled smoothly by diffusing the effective information from the undamaged region into the damaged region at the pixel level [35]. The representative approaches include the BSCB model [5], the TV model [7], and the CDD model [6].
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