| Summary: | Due to severe information degradation, underwater image deblurring remains a challenging ill-posed problem. However, most deep learning models do not adequately exploit the hierarchical features of the original underwater images. They typically use clear images as positive samples to guide the training of image enhancement networks, while neglecting the utilization of negative information. In this paper, we introduce the Residual Dense Block (RDB) and Contrastive Regularization (CR) techniques. By leveraging the local and global feature fusion of RDB and the contrastive learning of CR, our model effectively extracts multi-level features from the original images, adaptively preserves hierarchical features, and achieves high-quality underwater image deblurring through learning from the original images. Experimental results demonstrate that our model outperforms other comparative algorithms in terms of subjective visual quality and objective evaluation metrics across four datasets.
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