Work

Underwater image super-resolution via multi-domain learning

Year: 2025

Type: article

Abstract: Underwater images suffer from the haze effect and low contrast due to wavelength- and distance-dependent scattering and attenuation. These issues present significant challenges for various underwater vision tasks. Super-resolution (SR) of underwater images offers an effective solution, enhancing both detail refinement and overall image visibility. However, underwater image SR is challenging due to the serious degradation in image texture and color information. Aiming to enhance the performance of underwater image SR, this paper presents an underwater image SR network via multi-domain learning. Concretely, we first propose a multi-domain encoder network, which incorporates the gray and two color spaces into a unified structure. Such an architecture enables our proposed model improve underwater image quality by texture improvement and color correction. Coupled with the channel attention mechanism, the most discriminative features extracted from multiple domains are adaptively integrated and highlighted. Consequently, our network effectively boosts image resolution and improves the visual quality of underwater images by leveraging multi-domain data and the strengths of learning-based approaches. Comprehensive experiments confirm the superior performance of our proposed model in underwater image SR.

Source: Cognitive Robotics

DOI: 10.1016/j.cogr.2025.11.002

Authors: Guanze Shen, Jingxuan Zhang, Zhe Chen

Language: English

Topic: Image Enhancement Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $700