An interpretable dual-branch deep learning dehazing algorithm

Abstract:

In order to address the issues of detail loss and poor interpretability in current deep learning-based dehazing algorithms, this paper proposes an interpretable dual-branch deep learning dehazing algorithm. The algorithm employs a dual-branch collaborative architecture to decouple the dehazing task: the upper branch focuses on haze extraction through a designed haze removal block (HRB) that captures haze features in the frequency domain, while incorporating a channel attention mechanism to enhance feature extraction in dense haze regions. The lower branch adopts an aggregated residual framework for detail restoration to correct texture details lost during feature extraction. By computing the negative residual between the hazy image and the haze feature image, a preliminary dehazed image is obtained, which is then refined by the lower branch to produce the final dehazed result. Experiments on the SOTS, NH-HAZE, and real-world datasets demonstrate that compared to existing mainstream dehazing algorithms, the proposed method achieves more thorough haze removal, more complete detail preservation, and significant improvements in objective evaluation metrics. This work not only establishes new research directions for deep learning in image dehazing field but also provides a practical solution for real-world image clarity enhancement.