采用对比学习的多阶段Transformer图像去雾方法

A multi-stage Transformer network for image dehazing based on contrastive learning is proposed to solve the problem that existing image dehazing methods fail to achieve the desired results in local image dehazing and detail restoration, and the non-homogeneous haze cannot be removed completely all the way. First, the channel-wise Transformer block is utilized as the primary feature extraction block to adequately capture the mutual long-range dependencies among channels. Second, the multi-modality supervised contrastive learning is introduced to maximize the capturing efficiency of information from the contrastive samples, so that the restored image is closer to the clear image in the embedding space while staying as far away from the hazy image as possible. Finally, a hierarchical multi-patch structure and deformable Transformer blocks are employed to effectively integrate the local and global structural information of the hazy image. Moreover, a large number of tests have been conducted on the proposed method by using two synthetic data sets and the three real data sets. The results show that the proposed MSTCNet achieves a higher peak signal-to-noise ratio(PSNR)gain of 1.49, 1.45, 0.11, 1.45 and 0.22 dB on five datasets, respectively. It outperforms existing methods in both general and non-data sets, shows the best visual effect of dehazing in removing the dense, non-homogeneous and uniform haze, and achieves the highest objective evaluation index value.