The light absorption and scattering of underwater impurities lead to poor underwater imaging quality. The existing data-driven based underwater image enhancement (UIE) techniques suffer from the lack of a large-scale dataset containing various underwater scenes and high-fidelity reference images. Besides, the inconsistent attenuation in different color channels and space areas is not fully considered for boosted enhancement. In this work, we constructed a large-scale underwater image (LSUI) dataset including 5004 image pairs, and reported an U-shape Transformer network where the transformer model is for the first time introduced to the UIE task. The U-shape Transformer is integrated with a channel-wise multi-scale feature fusion transformer (CMSFFT) module and a spatial-wise global feature modeling transformer (SGFMT) module, which reinforce the network's attention to the color channels and space areas with more serious attenuation. Meanwhile, in order to further improve the contrast and saturation, a novel loss function combining RGB, LAB and LCH color spaces is designed following the human vision principle. The extensive experiments on available datasets validate the state-of-the-art performance of the reported technique with more than 2dB superiority.
翻译:水下杂质的光吸收和散射导致水下成像质量差。现有基于数据驱动的水下放大图像技术(UIE)缺乏包含各种水下场景和高纤维参考图像的大型数据集。此外,不同颜色信道和空间区域的衰减不一致并未被充分考虑增强。在这项工作中,我们建造了一个大型水下成像(LSIUI)数据集,包括5004个成像配对,并报告了一个Ushape变形器网络,其中变形器模型首次引入UIE任务。Ushape变形器与一个带频道的多级特征聚变异器模块和空间全球地貌模型变异器模块集成在一起,这些变色器和空间变异器的变色器没有被充分考虑增强。与此同时,为了进一步改善对比和饱和度,根据人类的视野原则,设计了将变色器模型、变色器和变色器的新型损失功能。所报告的高性能实验,比现有2级性能高。