期刊
IEEE TRANSACTIONS ON IMAGE PROCESSING
卷 30, 期 -, 页码 4985-5000出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2021.3076367
关键词
Image color analysis; Image enhancement; Imaging; Visualization; Decoding; Feature extraction; Scattering; Underwater imaging; image enhancement; color correction; scattering removal
资金
- Hong Kong RGC [CityU 21211518, 11219019, 11202320]
- Hong Kong GRF-RGC General Research Fund [9042958 (CityU 11203820), 9042816 (CityU 11209819)]
- Beijing Nova Program [Z201100006820016]
- National Natural Science Foundation of China [62002014]
- Young Elite Scientist Sponsorship Program by the China Association for Science and Technology [2020QNRC001]
- CAAI-Huawei MindSpore Open Fund
The Ucolor network enhances underwater images by incorporating multiple color spaces embedding and utilizing both physical model-based and learning-based methods. Experimental results show superior performance in visual quality and quantitative metrics compared to state-of-the-art methods.
Underwater images suffer from color casts and low contrast due to wavelength- and distance-dependent attenuation and scattering. To solve these two degradation issues, we present an underwater image enhancement network via medium transmission-guided multi-color space embedding, called Ucolor. Concretely, we first propose a multi-color space encoder network, which enriches the diversity of feature representations by incorporating the characteristics of different color spaces into a unified structure. Coupled with an attention mechanism, the most discriminative features extracted from multiple color spaces are adaptively integrated and highlighted. Inspired by underwater imaging physical models, we design a medium transmission (indicating the percentage of the scene radiance reaching the camera)-guided decoder network to enhance the response of network towards quality-degraded regions. As a result, our network can effectively improve the visual quality of underwater images by exploiting multiple color spaces embedding and the advantages of both physical model-based and learning-based methods. Extensive experiments demonstrate that our Ucolor achieves superior performance against state-of-the-art methods in terms of both visual quality and quantitative metrics. The code is publicly available at: https://li-chongyi.github.io/Proj_Ucolor.html.
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