Abstract:

To address the shortcomings of existing image dehazing algorithms， such as the lack of global contextual information， inadequate performance in dealing with non‑uniform haze， and the introduction of noise during the reuse of detailed information， a CNN-Transformer dehazing algorithm based on global residual attention and gated feature fusion is proposed. Firstly， a global residual attention mechanism is introduced to adaptively extract the detailed features from non‑uniform haze regions， and cross‑dimensional channel‑spatial attention is designed to optimize information weights. Thereafter， a global modelling Transformer module is proposed to deepen the feature extraction process of the encoder， and a Swin Transformer with parallel convolutions is constructed to capture the inter‑feature dependencies. Finally， a gated feature fusion decoder module is designed to reuse the texture information required for image reconstruction， to filter out irrelevant haze noise， and thereby to improve dehazing performance. Qualitative and quantitative experiments conducted on four publicly available datasets indicate that the proposed algorithm can effectively handle non‑uniform haze regions， reconstruct high‑fidelity haze‑free images with fine textures and rich semantics， and achieve higher peak signal‑to‑noise ratio and structural similarity index compared to the classic algorithm.

Key words: image dehazing, global residual attention mechanism, CNN-Transformer architecture, gated feature fusion, image reconstruction