Name
Abstract | ||
|---|---|---|
Image inpainting restores lost or deteriorated parts of images according to the information of known regions. Criminisi has proposed an effective exemplar-based inpainting method, which has the advantages of both texture synthesis and diffusion-based inpainting. Yet, it has its own flaws of fast priority dropping and visual inconsistency. In this paper, we propose a space varying updating strategy for the confidence term and a matching confidence term to improve the filling priority estimation. We propose structure consistent patch matching to take the distribution of source and target patch differences into account. Fast Fourier transform is adapted for full image searching to achieve better and faster matching results. Experimental results are given to demonstrate the improvements made by our proposed method. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.neucom.2016.08.149 | Neurocomputing |
Keywords | DocType | Volume |
Image inpainting,Exemplar-based,Patch matching,Fast Fourier transform | Journal | 269 |
Issue | ISSN | Citations |
C | 0925-2312 | 4 |
PageRank | References | Authors |
0.38 | 8 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haixia Wang | 1 | 132 | 27.85 |
| Yifei Cai | 2 | 4 | 0.38 |
| Ronghua Liang | 3 | 376 | 42.60 |
| Xiao-Xin Li | 4 | 31 | 2.78 |
| Li Jiang | 5 | 5 | 1.06 |