2021 IEEE International Conference on Acoustics, Speech and Signal Processing
6-11 June 2021 • Toronto, Ontario, Canada
Extracting Knowledge from Information
| Paper ID | IVMSP-19.4 | ||
| Paper Title | A FAST AND EFFICIENT NETWORK FOR SINGLE IMAGE DERAINING | ||
| Authors | Youzhao Yang, Hong Lu, Fudan University, China | ||
| Session | IVMSP-19: Deraining and Dehazing | ||
| Location | Gather.Town | ||
| Session Time: | Thursday, 10 June, 13:00 - 13:45 | ||
| Presentation Time: | Thursday, 10 June, 13:00 - 13:45 | ||
| Presentation | Poster | ||
| Topic | Image, Video, and Multidimensional Signal Processing: [IVARS] Image & Video Analysis, Synthesis, and Retrieval | ||
| IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
| Abstract | Rain streaks will degrade the visibility of images. To tackle this problem, we propose a novel Adaptive Dilated Network (ADN) to remove rain streaks from a single image while using less parameters and running faster than previous methods. Specifically, an Adaptive Dilated Block (ADB) is constructed as the sub-module of ADN. In ADB, we apply a shared dilated block to extract multi-scale features. Then a dilated selection block is added to leverage the importance of features in different scales. All the multi-scale features are fused together to obtain features with rich rain details. To further model the inter-dependencies of the fused features, a feature selection block is employed in ADB to assign different weights to each feature. Moreover, all the hierarchical features extracted by each ADB are concatenated together and fed into a rainy map generator to estimate rain layer. Experimental results demonstrate that the proposed method is superior to the state-of-the-art methods on performances and running time while using less parameters. The source code is available at https://github.com/nnUyi/ADN. | ||