  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.
