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.

翻译：水下图像面临复杂且多样的退化问题，这不可避免地影响了水下视觉任务的性能。然而，现有大多数基于学习的水下图像增强（UIE）方法主要在空间域中恢复此类退化，很少关注傅里叶频率信息。本文提出了一种基于空间-频率交互和梯度图的新型UIE框架，即SFGNet，该框架包含两个阶段。具体而言，在第一阶段，我们提出了一种密集空间-频率融合网络（DSFFNet），主要包括我们设计的密集傅里叶融合模块和密集空间融合模块，通过这两个模块间的交叉连接实现了充分的空间-频率交互。在第二阶段，我们提出了一种梯度感知校正器（GAC），通过梯度图进一步增强图像的感知细节和几何结构。在两个真实世界水下图像数据集上的实验结果表明，我们的方法能够成功增强水下图像，并在视觉质量提升方面取得了具有竞争力的性能。