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Not All Areas Are Equal: A Novel Separation‐Restoration‐Fusion Network for Image Raindrop Removal
Author(s) -
Ren Dongdong,
Li Jinbao,
Han Meng,
Shu Minglei
Publication year - 2020
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.14162
Subject(s) - fuse (electrical) , benchmark (surveying) , margin (machine learning) , computer science , artificial intelligence , image (mathematics) , fusion , image restoration , image fusion , feature (linguistics) , task (project management) , pattern recognition (psychology) , computer vision , machine learning , image processing , geography , philosophy , linguistics , management , geodesy , engineering , electrical engineering , economics
Abstract Detecting and removing raindrops from an image while keeping the high quality of image details has attracted tremendous studies, but remains a challenging task due to the inhomogeneity of the degraded region and the complexity of the degraded intensity. In this paper, we get rid of the dependence of deep learning on image‐to‐image translation and propose a separation‐restoration‐fusion network for raindrops removal. Our key idea is to recover regions of different damage levels individually, so that each region achieves the optimal recovery result, and finally fuse the recovered areas. In the region restoration module, to complete the restoration of a specific area, we propose a multi‐scale feature fusion global information aggregation attention network to achieve global to local information aggregation. Besides, we also design an inside and outside dense connection dilated network, to ensure the fusion of the separated regions and the fine restoration of the image. The qualitatively and quantitatively evaluations are conducted to evaluate our method with the latest existing methods. The result demonstrates that our method outperforms state‐of‐the‐art methods by a large margin on the benchmark datasets in extensive experiments.