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

TLDR

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. The Ucolor network uses a multi‑color‑space encoder that fuses features from different color spaces, an attention module that highlights discriminative features, and a medium‑transmission‑guided decoder that boosts quality‑degraded regions. Our experiments show that Ucolor improves visual quality and outperforms state‑of‑the‑art methods on both visual and quantitative metrics. The code is publicly available at https://li-chongyi.github.io/Proj_Ucolor.html.

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 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.