  It is challenging to remove rain-steaks from a single rainy image because the
rain steaks are spatially varying in the rainy image. Although the CNN based
methods have reported promising performance recently, there are still some
defects, such as data dependency and insufficient interpretation. A single
image deraining algorithm based on the combination of data-driven and
model-based approaches is proposed. Firstly, an improved weighted guided image
filter (iWGIF) is used to extract high-frequency information and learn the rain
steaks to avoid interference from other information through the input image.
Then, transfering the input image and rain steaks from the image domain to the
feature domain adaptively to learn useful features for high-quality image
deraining. Finally, networks with attention mechanisms is used to restore
high-quality images from the latent features. Experiments show that the
proposed algorithm significantly outperforms state-of-the-art methods in terms
of both qualitative and quantitative measures.
