CEWformer: A Transformer-Based Collaborative Network for Simultaneous Underwater Image Enhancement and Watermarking

Since the copyright of the enhanced underwater image should be protected, we propose a transformer-based collaborative network (CEWformer) for simultaneous underwater image enhancement and watermarking. In CEWformer, a channel self-attention transformer (CSAT) is deployed by mining channel correlations to enhance channels with severe color attenuation. To emphasize quality degradation and inconspicuous regions, a mixed self-attention transformer (MSAT) is also employed by computing both channel and spatial correlations for improving the image quality. Meanwhile, CEWformer integrates a watermark fusion transformer (WFT) to capture robust image features by modeling the cross-domain relationship between the image and watermark for increasing watermarking robustness. In addition, multiscale image and watermark features are fused to gain multiple watermark copies for increasing robustness as well. Extensive experimental results demonstrate that the proposed CEWformer can enhance the underwater image and embed a robust watermark simultaneously and effectively. Compared to existing underwater image enhancement methods, the visual quality of the proposed CEWformer is better, which shows the low effect of watermark embedding on the image quality. Furthermore, the proposed CEWformer is superior to existing image watermarking models in terms of watermarking robustness and invisibility.