DAUT: Underwater Image Enhancement Using Depth Aware U-shape Transformer

Images captured underwater are subject to different complex effects including absorption and scattering. Recovery of the original images is not a trivial task. The underwater image formation varies according to many dependencies. Also, underwater image enhancement models have been affected by the lack of large-scale underwater datasets with reference-enhanced images. Therefore, in this paper, we use cycleGAN to generate a new underwater dataset from existing in-air datasets. We train our data with a U-shape Transformer, which is one of the state-of-the-art models. We add a depth estimation module as object depth is one of the most vital dependencies to the underwater image formation model. The estimated depth and the underwater image are the input to our depth-aware U-transformer. Our experiments show that our model achieves state-of-the-art results in objective and subjective evaluations compared with the original U-shape Transformer and other state-of-the-art methods. the code and dataset are available at https://github.com/MBadran2000/Depth-Aware-U-shape-Transformer.git