Name
Abstract | ||
|---|---|---|
Vignetting is a common type of image degradation that makes peripheral parts of an image darker than the central part. Singleimage devignetting aims to remove undesirable vignetting from an image without resorting to calibration, thereby providing high-quality images required for a wide range of applications. Previous studies into single-image devignetting have focused on the estimation of vignetting functions under the assumption that degradation other than vignetting is negligible. However, noise in real-world observations remains unremoved after inversion of vignetting, and prevents stable estimation of vignetting functions, thereby resulting in low quality of restored images. In this paper, we introduce a methodology of image restoration based on variational Bayes (VB) to devignetting, aiming at high-quality devignetting in the presence of noise. Through VB inference, we jointly estimate a vignetting function and a latent image free from both vignetting and noise, using a general image prior for noise removal. Compared with state-of-the-art methods, the proposed VB approach to single-image devignetting maintains effectiveness in the presence of noise, as we demonstrate experimentally. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1587/transinf.2017EDP7393 | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS |
Keywords | DocType | Volume |
vignetting, devignetting, single-image, variational Bayes | Journal | E101D |
Issue | ISSN | Citations |
9 | 1745-1361 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Motoharu Sonogashira | 1 | 1 | 1.37 |
| Masaaki Iiyama | 2 | 17 | 14.23 |
| Michihiko Minoh | 3 | 349 | 58.69 |