A study on non-uniform image dehazing algorithm based on serialized integrated attention and multi-dimensional transformer

Abstract

To address the issues of detail loss and blurred restoration in the non-uniform image dehazing process of existing non-uniformly hazy images, this paper presents a novel non-uniform image dehazing algorithm based on serialized integrated attention and multi-dimensional Transformer. This approach aims to restore clear, detailed scenes from heavily hazy images. Firstly, a serialized integrated attention module is established to capture image features. This module amalgamates spatial and channel attention mechanisms and is applied to the shallow-layer network. It effectively concentrates on the local features of the image in both spatial and channel dimensions. Secondly, a multi-dimensional Transformer module is incorporated into the deep-layer network to extract global information and reduce information loss during feature extraction. Finally, feature network fusion is carried out to adaptively fuse the feature maps of the shallow layer and the deep layer. This allows the model to take into account local and global information, combine the detailed local features of the shallow layer with the broad global information of the deep layer, and capture fine-grained details while integrating the image context. The experimental results clearly demonstrate the effectiveness of the proposed algorithm. On the Ⅰ-HAZE, O-HAZE, and NH-HAZE non-uniform haze datasets, the algorithm achieves Peak Signal-to-Noise Ratio values of 22.86, 25.86, and 22.06, along with Structural Similarity Index Measurement values of 0.8731, 0.7799, and 0.7796, respectively. Moreover, the effectiveness of this algorithm is verified on real-world hazy images. Compared with other dehazing algorithms, our proposed method outperforms them in both visual effects and objective metrics.

Keywords

Non-uniform image dehazing / serialized integrated attention / multi-dimensional transformer / feature network fusion