Toward Sufficient Spatial-Frequency Interaction for Gradient-aware Underwater Image Enhancement

TL;DR

This paper introduces SFGNet, a novel underwater image enhancement framework that leverages spatial-frequency interaction and gradient maps to improve image quality, outperforming existing methods in visual clarity.

Contribution

The paper proposes a two-stage framework with dense spatial-frequency fusion and gradient-aware correction, addressing the lack of frequency domain consideration in underwater image enhancement.

Findings

Achieves competitive visual quality improvement on real-world datasets.

Effectively enhances perceptual details and geometric structures.

Demonstrates the importance of spatial-frequency interaction in UIE.

Abstract

Underwater images suffer from complex and diverse degradation, which inevitably affects the performance of underwater visual tasks. However, most existing learning-based Underwater image enhancement (UIE) methods mainly restore such degradations in the spatial domain, and rarely pay attention to the fourier frequency information. In this paper, we develop a novel UIE framework based on spatial-frequency interaction and gradient maps, namely SFGNet, which consists of two stages. Specifically, in the first stage, we propose a dense spatial-frequency fusion network (DSFFNet), mainly including our designed dense fourier fusion block and dense spatial fusion block, achieving sufficient spatial-frequency interaction by cross connections between these two blocks. In the second stage, we propose a gradient-aware corrector (GAC) to further enhance perceptual details and geometric structures of images by gradient map. Experimental results on two real-world underwater image datasets show that our approach can successfully enhance underwater images, and achieves competitive performance in visual quality improvement. The code is available at https://github.com/zhihefang/SFGNet.