Multi-Attention Mechanism for Raindrop Removal from a Single Image

The image raindrop removal task aims to remove raindrops attached to the lens from a given rainy image, restoring a clean image, which plays a crucial role in downstream tasks of computer vision. Because the existing image raindrop removal methods not taking the spatial locality and scale diversity of raindrops into account, the raindrops are usually not completely removed from the images. To alleviate the above issues, this paper proposes a raindrop removal method from a single image based on a multi-attention mechanism. First, in order to adapt to the spatial locality and scale diversity of raindrops, a multi-scale feature extraction module and a multi-attention module are combined to construct an encoder-decoder architecture. The multi-attention module integrates pixel, channel, and spatial attention, which can match the spatial locality of raindrops adaptively. In addition, this paper designs an iterative image feature fusion module. The features from the decoder and raindrop images are fused to obtain a preliminary raindrop removal image; then, the decoder features are enhanced with the preliminary raindrop removal image to obtain further refined features; the preliminary raindrop removal image and refined features are fused to obtain a final raindrop removal image. The experimental results on Raindrop image test set show that the proposed method can effectively remove raindrops from the image compared with other methods, further improving the performance of raindrop removal, and improving the PSNR by 0.25 dB compared to the best method in the comparison methods.