Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding

• URL: http://arxiv.org/abs/2104.13015v1
• Date: Tue, 27 Apr 2021 07:35:30 GMT
• Title: Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding
• Authors: Chongyi Li and Saeed Anwar and Junhui Hou and Runmin Cong and Chunle Guo and Wenqi Ren
• Abstract summary: We present an underwater image enhancement network via medium transmission-guided multi-color space embedding, called Ucolor. Our network can effectively improve the visual quality of underwater images by exploiting multiple color spaces embedding.
• Score: 88.46682991985907
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 the 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.

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