Self-Guided Region-aware Causal Collaborative Contrastive Network with Positional-Embedded Channel Attention for image dehazing

Le résumé fourni par la source

Recently, unsupervised learning has emerged as a significant direction in image dehazing research. However, existing methods still suffer from inherent limitations. In dual-branch cyclic frameworks, the generator processes inputs from different distributions simultaneously, leading to learning confusion. Moreover, physical disentanglement frameworks impose insufficient constraints on the haze components, thereby limiting their ability to accurately reconstruct scene details. Meanwhile, traditional channel attention mechanisms rely solely on global average pooling to extract channel representations, resulting in substantial information loss and consequently leading to suboptimal allocation of channel weights related to dehazing. To address the above issues, we propose a Self-Guided Region-aware Causal Collaborative Contrastive Network with Positional-Embedded Channel Attention (SGRPC-Net) for image dehazing. Specifically, we design a Self-Guided Region-aware Causal Collaborative Contrastive Framework (SRCF) that establishes region-aware collaborative contrastive constraints and causal counterfactual contrastive constraints. This framework not only alleviates the learning confusion inherent in dual-branch cyclic architectures but also enables adaptive regulation of dehazing intensity across different regions. Furthermore, it enhances the constraint capability on haze components at the feature level, thereby improving the network’s reconstruction performance. In addition, we design a Positional Embedded Channel Attention (PCA) module that integrates the captured horizontal and vertical positional information into channel representations, compensating for the loss of positional information caused by global pooling and enabling more precise channel weight allocation. Experimental results on challenging benchmark datasets demonstrate that our SGRPC-Net outperforms state-of-the-art unsupervised image dehazing methods. The source code is available at https://github.com/Lose-Code/SGRPC-Net .

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