Name
Abstract | ||
|---|---|---|
AbstractImages captured in low lighting environment suffer from both low luminance contrast and noise corruption. However, most existing contrast enhancement algorithms only consider contrast boosting, which tends to reveal or amplify noise that is originally not visible in the dark areas. In this paper, we propose a joint contrast enhancement and denoising algorithm, which is based on structure/texture layer decomposition via minimization of dual forms of graph total variation (GTV). Specifically, the structure layer is expected to be generally smoothing but with sharp edges at the foreground background boundaries, for which we propose a quadratic form of GTV (QGTV) as the prior that promotes signal smoothness along graph structure. For the texture layer, a re-weighted GTV (RGTV) is tailored to noise removal while preserving true image details. We provide theoretical analysis about the filtering behavior of these two priors. Furthermore, a boost factor is derived per patch via optimal contrast-tone mapping to improve the overall brightness level of the patch. Finally, an optimization objective function is formulated, which casts image decomposition, brightness boosting, and noise reduction into a unified optimization framework. We further propose a fast approach to efficiently solve the optimization and provide analysis about the convergency. The experimental results show that the proposed method outperforms the state-of-the-art works in subjective, objective, and statistical quality evaluation. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TCSVT.2019.2924454 | Periodicals |
Keywords | DocType | Volume |
Image decomposition, Noise reduction, Brightness, Image edge detection, Optimization, Lighting, Additives, Contrast enhancement, image denoising, image decomposition, graph signal modeling, graph total variation | Journal | 30 |
Issue | ISSN | Citations |
8 | 1051-8215 | 2 |
PageRank | References | Authors |
0.36 | 8 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xianming Liu | 1 | 461 | 47.55 |
| Deming Zhai | 2 | 141 | 13.44 |
| Yuanchao Bai | 3 | 34 | 4.36 |
| Xiangyang Ji | 4 | 533 | 73.14 |
| Wen Gao | 5 | 11374 | 741.77 |