Rain in the acquired images can cause problems such as loss of details and blurry textures, which is not conducive to the analysis and research of subsequent computer vision tasks. To remove rain stripe from images and obtain clean background images, a hybrid CNN-Transformer single image deraining network based on attention mechanism is proposed. Firstly, shallow feature extraction is performed by the first 5 layers of Resnet18. Then, the high and low frequency rain stripe detection module is introduced to generate rain stripe attention maps to promote the subsequent network focus on rain stripes by Laplacian operator and global processor. Besides, an improved hybrid CNN-Transformer module is formed to fully extract image features by incorporating channel and spatial attention into the traditional Transformer structure. Finally, feature reconstruction is achieved through pixel sampling to obtain deraining images. Compared with other mainstream deraining methods on common datasets, the results show that the proposed network achieves better quantitative indicators and visual effects, confirming the effectiveness of the proposed method.
Key words
image deraining /
high and low frequency rain stripe detection /
hybrid CNN-Transformer
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