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

TL;DR

This paper introduces Ucolor, an underwater image enhancement network that combines multi-color space embedding and medium transmission guidance to effectively improve underwater image quality, outperforming existing methods.

Contribution

The paper proposes a novel multi-color space encoder with attention and a medium transmission-guided decoder, integrating physical models with learning for superior underwater image enhancement.

Findings

Ucolor outperforms state-of-the-art methods in visual quality.

The multi-color space encoder enhances feature diversity.

Medium transmission guidance improves focus on degraded regions.

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.