双重注意力引导的弱监督雨滴图像增强

基于深度学习的雨滴图像增强方法普遍存在高度依赖配对样本数据集，雨滴去除后图像背景细节模糊等问题。对此，本文提出一种双重注意力引导的弱监督雨滴图像增强方法。该方法设计构建弱监督雨滴图像增强网络，仅需来自雨滴图像域与干净图像域的图像进行训练，可有效降低对配对样本数据集的依赖性；同时，将双重注意力引入生成网络，引导特征提取与多分支掩模生成，掩模同输入的雨滴图像融合后，获得背景清晰的干净图像，实现雨滴图像增强。实验结果表明，该方法在Raindrop数据集上PSNR达到27.0711 dB，SSIM达到0.8996，更好地保留了图像背景细节与颜色信息，证明该方法的可行性与有效性。

Weakly supervised raindrop image enhancement guided by dual attention

Raindrop image enhancement methods based on deep learning generally have some problems, such as highly dependent on aligned sample datasets, and blurred background detail after removing raindrops. In this regard, this paper proposes a weakly supervised raindrop image enhancement method guided by dual attention. This method designs and constructs a weakly supervised raindrop image enhancement network. It only needs images from the raindrop image domain and the clean image domain for training, which can effectively reduce the dependence on the aligned sample datasets. At the same time, dual attention is introduced into the generation network to guide the feature extraction and multi-branch masks generation. After the masks are fused with the input raindrop image, a clean image with a clear background is obtained, and the input raindrop image is enhanced. The experimental results show that the PSNR is 27.0711 dB and the SSIM is 0.8996 of the proposed method respectively on the Raindrop. The background details and color information of the image are better preserved than the previous methods, which prove the feasibility and effectiveness of the proposed method.