LAUSR

For the sake of removing the rain attached to rain images to restore the clarity of the images and due to the fact that existing rain removal methods cannot effectively remove rain from real-world images, a rain removal method for real-world images fusing deep learning and image enhancement is proposed. Firstly, a deep convolutional neural network based on supervised learning idea, multi-recursive LSTM and Spatial-Attention Mechanism is constructed to remove rain from real-world images. Then, the Rain Located and Filtered Algorithm is designed to further remove residual rain from derained images. Finally, the Visual Effect Improved Algorithm is proposed to improve the contrast of derained images and enhance the visual effect of derained images. The experimental results show that compared with the representative single image rain removal methods, the proposed method can not only effectively remove rain from real-world images, but also remove rain from synthetic images. As a result, the proposed method can make the processed images retain more detailed information and provide the better visual effect.