SGUIE-Net: Semantic Attention Guided Underwater Image Enhancement with Multi-Scale Perception

TL;DR

SGUIE-Net is a novel underwater image enhancement model that uses semantic guidance and multi-scale perception to improve color and detail restoration, especially for rare degradation types, with promising experimental results.

Contribution

Introduces a semantic region-wise enhancement module and a guidance mechanism that leverages semantic information for robust underwater image enhancement.

Findings

Achieves superior enhancement quality on public and new datasets.

Effectively handles diverse and rare degradation types.

Demonstrates robustness and visual appeal in enhanced images.

Abstract

Due to the wavelength-dependent light attenuation, refraction and scattering, underwater images usually suffer from color distortion and blurred details. However, due to the limited number of paired underwater images with undistorted images as reference, training deep enhancement models for diverse degradation types is quite difficult. To boost the performance of data-driven approaches, it is essential to establish more effective learning mechanisms that mine richer supervised information from limited training sample resources. In this paper, we propose a novel underwater image enhancement network, called SGUIE-Net, in which we introduce semantic information as high-level guidance across different images that share common semantic regions. Accordingly, we propose semantic region-wise enhancement module to perceive the degradation of different semantic regions from multiple scales and feed it back to the global attention features extracted from its original scale. This strategy helps to achieve robust and visually pleasant enhancements to different semantic objects, which should thanks to the guidance of semantic information for differentiated enhancement. More importantly, for those degradation types that are not common in the training sample distribution, the guidance connects them with the already well-learned types according to their semantic relevance. Extensive experiments on the publicly available datasets and our proposed dataset demonstrated the impressive performance of SGUIE-Net. The code and proposed dataset are available at: https://trentqq.github.io/SGUIE-Net.html