Controlling the Rain: From Removal to Rendering

Siqi Ni, Xueyun Cao, Tao Yue, Xuemei Hu; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 6328-6337


Existing rain image editing methods focus on either removing rain from rain images or rendering rain on rain-free images. This paper proposes to realize continuous control of rain intensity bidirectionally, from clear rain-free to downpour image with a single rain image as input, without changing the scene-specific characteristics, e.g. the direction, appearance and distribution of rain. Specifically, we introduce a Rain Intensity Controlling Network (RICNet) that contains three sub-networks of background extraction network, high-frequency rain-streak elimination network and main controlling network, which allows to control rain image of different intensities continuously by interpolation in the deep feature space. The HOG loss and autocorrelation loss are proposed to enhance consistency in orientation and suppress repetitive rain streaks. Furthermore, a decremental learning strategy that trains the network from downpour to drizzle images sequentially is proposed to further improve the performance and speedup the convergence. Extensive experiments on both rain dataset and real rain images demonstrate the effectiveness of the proposed method.

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@InProceedings{Ni_2021_CVPR, author = {Ni, Siqi and Cao, Xueyun and Yue, Tao and Hu, Xuemei}, title = {Controlling the Rain: From Removal to Rendering}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2021}, pages = {6328-6337} }