Joint Priors-Based Restoration Method for Degraded Images Under Medium Propagation

The deep learning-based methods have shown promising performance in restoring degraded images such as underwater and haze images. However, the majority of existing methods rely on simplified imaging models, which limits their generalization and applicability in real-world scenarios. To address these issues, we incorporate the imaging mechanism in complex underwater environments to redefine the imaging model for degraded images under medium propagation. We then propose a multi-stage restoration framework that combines model-based iterative optimization methods and deep learning methods. At the same time, to tackle the problem of inaccurate parameter estimation in methods relying on a single prior, we introduce a regularization design based on joint priors and develop an attention-based color correction network to correct color distortions in the degraded images. Experimental results on real-world degraded images demonstrate the effectiveness and superiority of our method in both quantitative and subjective evaluations when compared to state-of-the-art methods.