Adaptive haze pixel intensity perception transformer structure for image dehazing networks.

Abstract

In the realm of deep learning-based networks for dehazing using paired clean-hazy image datasets to address complex real-world haze scenarios in daytime environments and cross-dataset challenges remains a significant concern due to algorithmic inefficiencies and color distortion. To tackle these issues, we propose SwinTieredHazymers (STH), a dehazing network designed to adaptively discern pixel intensities in hazy images and compute haze residue for clarity restoration. Through a unique three-branch design, we hierarchically modulate haze residuals by leveraging the global features brought by Transformer and the local features brought by Convolutional Neural Network (CNN) which has led to the algorithm's widespread applicability. Experimental results demonstrate that our approach surpasses advanced single-image dehazing methods in both quantitative metrics and visual fidelity for real-world hazy image dehazing, while also exhibiting strong performance in cross-dataset dehazing scenarios.