Name
Abstract | ||
|---|---|---|
Object removal can be accomplished by an image inpainting process which obtains a visually plausible image interpolation of an occluded or damaged region. There are two key components in an exemplar-based image inpainting approach: computing filling priority of patches in the missing region and searching for the best matching patch. In this paper, we present a robust exemplar-based method. In the improved model, a regularized factor is introduced to adjust the patch priority function. A modified sum of squared differences (SSD) and normalized cross correlation (NCC) are combined to search for the best matching patch. We evaluate the proposed method by applying it to real-life photos and testing the removal of large objects. The results demonstrate the effectiveness of the approach. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.neucom.2013.06.022 | Neurocomputing |
Keywords | Field | DocType |
Object removal,Image inpainting,Exemplar,Filling priority,Similarity | Cross-correlation,Computer vision,Square (algebra),Pattern recognition,Inpainting,Artificial intelligence,Mathematics,Image scaling,Machine learning | Journal |
Volume | Issue | ISSN |
123 | null | 0925-2312 |
Citations | PageRank | References |
4 | 0.42 | 9 |
Authors | ||
5 | ||