Name
Abstract | ||
|---|---|---|
We provide an efficient image-denoising prior, spatial-gradient-local-inhomogeneity (SGLI), which can be successfully applied to image reconstruction. The SGLI prior employs two complementary discontinuity measures: spatial gradient and local inhomogeneity. The spatial gradient measures effectively preserves strong edge components of images, while the local inhomogeneity measure successfully detects locations of the significant discontinuities considering uniformity of small regions. The two complementary measures are elaborately combined into the SGLI prior for image denoising. Thus, the SGLI prior effectively preserves feature components such as edges and textures of images while reducing noise. Comparative results indicate that the proposed SGLI prior is very effective in dealing with the image denoising problem from corrupted images. (C) 2010 SPIE and IS&T. [DOI: 10.1117/1.3466800] |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1117/1.3466800 | JOURNAL OF ELECTRONIC IMAGING |
Keywords | Field | DocType |
null | Iterative reconstruction,Computer vision,Classification of discontinuities,Pattern recognition,Computer science,Discontinuity (linguistics),Image denoising,Artificial intelligence | Journal |
Volume | Issue | ISSN |
19 | 3 | 1017-9909 |
Citations | PageRank | References |
4 | 0.46 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cheolkon Jung | 1 | 342 | 47.75 |
| Licheng Jiao | 2 | 5698 | 475.84 |
| HyungSeok Kim | 3 | 116 | 22.09 |
| Joongkyu Kim | 4 | 98 | 13.83 |