Name
Abstract | ||
|---|---|---|
Underwater images generally suffer from low contrast, serious noise and color distortion. The main challenges of underwater image enhancement are to preserve details in dark regions while avoiding oversaturetion in bright regions. This paper proposes a novel underwater image enhancement method based on image decomposition. By decomposing the high-frequency texture and noise into the texture layer, the transmission map is estimated from the noise-free structure layer to avoid the noise amplification problem in underwater image enhancement. Both the structure layer and texture layer are descattered with the estimated transmission map. After denoising by gradient residual minimizition, the texture layer is enhanced and added back into the structure layer to recover the final enhanced image. Experimental results verify that the proposed approach can recover the high-quality images with fine details and edges while improving contrast and color naturalness, especially for images taken in the high turbidity environment. |
Year | Venue | Keywords |
|---|---|---|
2017 | 2017 24TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | Underwater images, color correction, structure-texture decomposition, contrast enhancement, de-noising |
Field | DocType | ISSN |
Noise reduction,Computer vision,Residual,Colors of noise,Pattern recognition,Computer science,Naturalness,Artificial intelligence,Image restoration,Distortion,Underwater | Conference | 1522-4880 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jingyu Yang | 1 | 274 | 31.04 |
| Xinyan Wang | 2 | 0 | 0.34 |
| Huanjing Yue | 3 | 24 | 6.89 |
| Xiaomei Fu | 4 | 22 | 7.12 |
| Chunping Hou | 5 | 85 | 14.69 |