Name
Abstract | ||
|---|---|---|
Underwater images suffer from various degradation, which can significantly lower the visual quality and the accuracy of subsequent applications. Moreover, the artificial light source tends to invalidate many image restoration algorithms. In this paper, an underwater image restoration (UIR) method using a novel Convolutional Neural Network (CNN) architecture and a synthesized underwater dataset is proposed. We discuss the reason for the over enhancement that exists in current UIR methods and revise the underwater image formation model (IFM) to alleviate the problem. With the revised IFM, we proposed an underwater image synthesizing method that can create a realistic underwater dataset. In order to effectively conduct end-to-end supervised learning, we design a network based on the characteristics of image restoration tasks, namely FMSNet. Different from existing networks, the decomposition and fusion operation in FMSNet can process the feature maps more efficiently and improve the contrast more prominently. The UIR method built by FMSNet can directly recover the degraded underwater images without the need of any pre-processing and post-processing. The experimental results indicate that FMSNet performs favorably against the widely used network architectures and our UIR method can outperform the state-of-the-art methods on both qualitative and quantitative evaluations. Comparing with the original underwater images, the experiments carried out by subsequent mission shows that 285% more feature points can be detected in the restored images by using our method. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-86365-4_34 | ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2021, PT III |
Keywords | DocType | Volume |
Neural network, Image restoration, CNN architecture, Image formation model | Conference | 12893 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiangyu Yin | 1 | 0 | 0.34 |
| Xiaohong Liu | 2 | 11 | 4.33 |
| Huan Liu | 3 | 2 | 2.06 |