Single Image Deraining via Feature-based Deep Convolutional Neural Network
This addresses the challenge of single image deraining for computer vision applications, but it appears incremental as it builds on existing CNN and filtering approaches.
The paper tackled the problem of removing rain streaks from a single rainy image by proposing a hybrid data-driven and model-based algorithm, which significantly outperformed state-of-the-art methods in qualitative and quantitative measures.
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.