  Underwater Image Enhancement (UIE) technology aims to tackle the challenge of
restoring the degraded underwater images due to light absorption and
scattering. To address problems, a novel U-Net based Reinforced Swin-Convs
Transformer for the Underwater Image Enhancement method (URSCT-UIE) is
proposed. Specifically, with the deficiency of U-Net based on pure
convolutions, we embedded the Swin Transformer into U-Net for improving the
ability to capture the global dependency. Then, given the inadequacy of the
Swin Transformer capturing the local attention, the reintroduction of
convolutions may capture more local attention. Thus, we provide an ingenious
manner for the fusion of convolutions and the core attention mechanism to build
a Reinforced Swin-Convs Transformer Block (RSCTB) for capturing more local
attention, which is reinforced in the channel and the spatial attention of the
Swin Transformer. Finally, the experimental results on available datasets
demonstrate that the proposed URSCT-UIE achieves state-of-the-art performance
compared with other methods in terms of both subjective and objective
evaluations. The code will be released on GitHub after acceptance.
