A multi-scale de-hazing network combining attention mechanisms
Qiannan Li, Yongfeng Qi · 2024
At present, there are some problems in traditional image dehazing algorithms, such as prior knowledge limitation, color distortion, high computational complexity and inability to process multi-scale information. This paper proposes an image dehazing network based on multi-scale lifting and attention mechanism fusion U-Net structure. Firstly, the downsampling operation is used to obtain multi-scale feature maps, and the feature maps of the encoder and decoder are connected by jump connection to realize feature fusion. The purpose is to transfer and fuse information between feature maps of different scales to improve the performance of the model. At the same time, in order to better aggregate cross-window information, an overlapping cross-attention module is introduced to enhance the information complementarity of adjacent window functions. By comparing the proposed algorithm with the other five algorithms in the public datasets Reside and NTIRE, the experimental results show that the proposed algorithm performs well in defogging effect and color retention. Specifically, the average peak signal to noise ratio (PSNR) of the proposed algorithm reaches 27.5 dB, and the average structure similarity (SSIM) is 0.9573. Compared with the other five algorithms, the performance has been significantly improved. Compared with the best comparison algorithm, the PSNR of the proposed algorithm is improved by 2.17 dB, and the SSIM is improved by 0.0063. These experimental results fully prove that the proposed algorithm has excellent performance in dehazing performance and color retention.