FENet: Underwater Image Enhancement via Frequency Domain Enhancement and Edge-Guided Refinement

Underwater images often affect the effectiveness of underwater visual tasks due to problems such as light scattering, color distortion, and detail blurring, limiting their application performance. Existing underwater image enhancement methods, although they can improve the image quality to some extent, often lead to problems such as detail loss and edge blurring. To address these problems, we propose FENet, an efficient underwater image enhancement method. FENet first obtains three different scales of images by image downsampling and then transforms them into the frequency domain to extract the low-frequency and high-frequency spectra, respectively. Then, a distance mask and a mean mask are constructed based on the distance and magnitude mean for enhancing the high-frequency part, thus improving the image details and enhancing the effect by suppressing the noise in the low-frequency part. Affected by the light scattering of underwater images and the fact that some details are lost if they are directly reduced to the spatial domain after the frequency domain operation. For this reason, we propose a multi-stage residual feature aggregation module, which focuses on detail extraction and effectively avoids information loss caused by global enhancement. Finally, we combine the edge guidance strategy to further enhance the edge details of the image. Experimental results indicate that FENet outperforms current state-of-the-art underwater image enhancement methods in quantitative and qualitative evaluations on multiple publicly available datasets.