Efficient Wavelet Boost Learning-Based Multi-stage Progressive Refinement Network for Underwater Image Enhancement

Abstract

Raw underwater images suffer from low contrast and color cast due to wavelength-selective light scattering and attenuation. The distortions in color and luminance mainly appear at the low frequency while that in edge and texture are mainly at the high frequency. However, the hybrid distortions are difficult to simultaneously recover for existing methods, which mainly focus on the spatial domain. To tackle these issues, we propose a novel deep learning network to progressively refine underwater images by wavelet boost learning strategy (PRWNet), both in spatial and frequency domains. Specifically, the Multi-stage refinement strategy is adopted to efficiently enhance the spatial-varying degradations in a coarse-to-fine way. For each refinement procedure, Wavelet Boost Learning (WBL) unit decomposes the hierarchical features into high and low frequency and enhances them respectively by normalization and attention mechanisms. The modified boosting strategy is also adopted in WBL to further enhance the feature representations. Extensive experiments show that our method achieves state-of-the-art results. Our network is efficient and has the potential for real-world applications. The code is available at: https://github.com/huofushuo/PRWNet.