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

• URL: http://arxiv.org/abs/2309.04089v2
• Date: Sun, 21 Jan 2024 12:32:04 GMT
• Title: Toward Sufficient Spatial-Frequency Interaction for Gradient-aware Underwater Image Enhancement
• Authors: Chen Zhao, Weiling Cai, Chenyu Dong, Ziqi Zeng
• Abstract summary: We develop a novel Underwater image enhancement (UIE) framework based on spatial-frequency interaction and gradient maps. Experimental results on two real-world underwater image datasets show that our approach can successfully enhance underwater images.
• Score: 5.553172974022233
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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