Deep Supervised Residual Dense Network for Underwater Image Enhancement
| Autor: | Yanling Han, Jing Wang, Lihua Huang, Yun Zhang, Cao Shouqi, Zhonghua Hong |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
02 engineering and technology TP1-1185 Residual Biochemistry Article residual Analytical Chemistry Image (mathematics) 0202 electrical engineering electronic engineering information engineering Computer vision Electrical and Electronic Engineering Underwater Instrumentation business.industry Deep learning deep supervision Chemical technology 020208 electrical & electronic engineering dense underwater image enhancement Atomic and Molecular Physics and Optics GAN details Feature (computer vision) Path (graph theory) 020201 artificial intelligence & image processing Artificial intelligence business Encoder Feature learning |
| Zdroj: | Sensors, Vol 21, Iss 3289, p 3289 (2021) Sensors Volume 21 Issue 9 Sensors (Basel, Switzerland) |
| ISSN: | 1424-8220 |
| Popis: | Underwater images are important carriers and forms of underwater information, playing a vital role in exploring and utilizing marine resources. However, underwater images have characteristics of low contrast and blurred details because of the absorption and scattering of light. In recent years, deep learning has been widely used in underwater image enhancement and restoration because of its powerful feature learning capabilities, but there are still shortcomings in detailed enhancement. To address the problem, this paper proposes a deep supervised residual dense network (DS_RD_Net), which is used to better learn the mapping relationship between clear in-air images and synthetic underwater degraded images. DS_RD_Net first uses residual dense blocks to extract features to enhance feature utilization then, it adds residual path blocks between the encoder and decoder to reduce the semantic differences between the low-level features and high-level features finally, it employs a deep supervision mechanism to guide network training to improve gradient propagation. Experiments results (PSNR was 36.2, SSIM was 96.5%, and UCIQE was 0.53) demonstrated that the proposed method can fully retain the local details of the image while performing color restoration and defogging compared with other image enhancement methods, achieving good qualitative and quantitative effects. |
| Databáze: | OpenAIRE |
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