Name
Abstract | ||
|---|---|---|
In this paper, we propose a global sparse gradient guided variational Retinex model (GSG-VR) for image enhancement. Based on the Retinex theory, a new variational Retinex model is proposed to decompose an image into illumination layer and reflectance layer. The gradient of illumination layer is expected to approximate a guided gradient field which is estimated by a global sparse gradient model (GSG). To estimate the guided gradient at each pixel, GSG makes use of pixels within its neighborhood (even global image). And a sparse regularization is imposed on the whole gradient field. These two models, the new variational Retinex and GSG model, compose a complete system GSG-VR. To solve it, a proximal forward–backward splitting algorithm and an alternating minimization algorithm are developed. A few numerical examples are presented to illustrate the effectiveness of the proposed models and algorithms. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.image.2017.08.008 | Signal Processing: Image Communication |
Keywords | Field | DocType |
Global sparse gradient,Retinex theory,Variational model,Image enhancement,Alternating minimization | Gradient method,Computer vision,Color constancy,Computer science,Vector field,Regularization (mathematics),Pixel,Artificial intelligence,Reflectivity,Minimization algorithm | Journal |
Volume | Issue | ISSN |
58 | C | 0923-5965 |
Citations | PageRank | References |
4 | 0.41 | 22 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rui Zhang | 1 | 4 | 1.08 |
| Xiang-Chu Feng | 2 | 989 | 40.18 |
| Lixia Yang | 3 | 4 | 0.74 |
| Lihong Chang | 4 | 9 | 1.16 |
| Chen Xu | 5 | 269 | 29.36 |