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Cited by 328

The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.

Uncertainty Guided Multi-Scale Residual Learning-Using a Cycle Spinning CNN for Single Image De-Raining

Open access
Yasarla, Rajeev, Patel, Vishal M.·2019·2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Abstract

Single image de-raining is an extremely challenging problem since the rainy image may contain rain streaks which may vary in size, direction and density. Previous approaches have attempted to address this problem by leveraging some prior information to remove rain streaks from a single image. One of the major limitations of these approaches is that they do not consider the location information of rain drops in the image. The proposed Uncertainty guided Multi-scale Residual Learning (UMRL) network attempts to address this issue by learning the rain content at different scales and using them to estimate the final de-rained output. In addition, we introduce a technique which guides the network to learn the network weights based on the confidence measure about the estimate. Furthermore, we introduce a new training and testing procedure based on the notion of cycle spinning to improve the final de-raining performance. Extensive experiments on synthetic and real datasets to demonstrate that the proposed method achieves significant improvements over the recent state-of-the-art methods.

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Metadata

Authors
Yasarla, R., & Patel, V. M.
Venue
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher
IEEE
Published
2019-06
Pages
8397-8406
ISSN
1063-6919 (ISSN-L)
OpenAlex
W2954051811
Cited by
328
References
34
FWCI
12.14
Type
conference paper
Language
en
Open access
green

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Yasarla, R., & Patel, V. M. (2019). Uncertainty Guided Multi-Scale Residual Learning-Using a Cycle Spinning CNN for Single Image De-Raining. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2019.00860

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