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

By Chongyi Li, Saeed Anwar, Junhui Hou, Runmin Cong, Chunle Guo, Wenqi Ren. IEEE Transactions on Image Processing, 2021

912 citations recorded by the source corpus.

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.

Subfields: Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/6bc2086ab437108bd88166578547791aa11f6d18

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Graphics and Computer-Aided Design

1. Chunle Guo
2. Chongyi Li

In Computer Vision and Pattern Recognition

1. Chongyi Li
2. Chunle Guo
3. Chunli Zhu
4. Liheng Bian
5. Lintao Peng
6. Runmin Cong
7. Peixian Zhuang
8. Sam Kwong

Citations in the corpus

• PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN With Dual-Discriminators (2023, methodology): “Following the settings in [31], the training data include 800 pairs of underwater images selected from the UIEB dataset [2] and 1250 synthetic underwater images”
• Underwater Image Enhancement via Weighted Wavelet Visual Perception Fusion (2024, background): “Recently, deep learning methods [10], [11], [12], [13] have been gradually applied to underwater image enhancement thanks to the massive amount of training data.”
• U-Shape Transformer for Underwater Image Enhancement (2021, methodology): “UGAN [9] and Ucolor [24] are selected for visual comparison considering their performance in full-reference experiment.”