UGIF-Net: An Efficient Fully Guided Information Flow Network for Underwater Image Enhancement

Jingchun Zhou, Boshen Li, Dehuan Zhang, Jieyu Yuan, Weishi Zhang, Zhanchuan Cai, Jinyu Shi

Abstract: Light traveling through water results in strong scattering across color channels, restricting visibility in underwater images. Many cutting-edge underwater image enhancement methods encounter limitations in color recovery accuracy and resilience against irrelevant feature interference. To tackle these degradation challenges, we propose an efficient and fully guided information flow network (UGIF-Net), for enhancing underwater images. Specifically, we propose a multicolor space-guided color estimation module that accurately approximates color information by incorporating features from two color spaces within a unified network. Subsequently, we employ a dense attention block (DAB) to guide the network in thoroughly extracting color information from both color spaces while adaptively perceiving crucial color information. Moreover, we devise a color-guided map to steer the network’s focus toward color information and augment its response to color quality degradation. We incorporate the guided map into a guide color restoration module to achieve visually appealing enhancement results. Comprehensive experiments indicate that our approach surpasses state-of-the-art methods, showcasing favorable image restoration effects and their potential to aid other high-level vision tasks.