Name
Title | ||
|---|---|---|
Enhancement of underwater images by super-resolution generative adversarial networks. |
Abstract | ||
|---|---|---|
Underwater image enhancement plays an important role in oceanic engineering, while the research is far from enough. The problems like color casts, low contrast brought out by the properties of water and its impurities, make it a challenging task. This paper proposes a novel framework for enhancing underwater image. It includes two parts, that is, pre-processing and de-blurring with improved Super-resolution Generative Adversarial Networks. First, in the process of pre-processing, we use the color correction approach and the contrast enhancement method to compensate color casts and produce natural color images. Second, an improved Super-resolution Generative Adversarial Networks is applied to pre-processed images in order to remove blurring and boost detail. Based on the network, the loss net is modified, so that the pre-processed images will be de-blurred and sharpened. The experimental results show that the proposed strategy improves the quality of underwater images efficiently. |
Year | Venue | Field |
|---|---|---|
2018 | ICIMCS | Computer vision,Pattern recognition,Computer science,Color correction,Artificial intelligence,Generative grammar,Superresolution,Underwater,Adversarial system |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
1 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Na Cheng | 1 | 0 | 0.34 |
| Tongtong Zhao | 2 | 21 | 6.33 |
| Zhengyu Chen | 3 | 11 | 8.41 |
| Xianping Fu | 4 | 71 | 23.89 |