The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Removing Rain from a Single Image via Discriminative Sparse Coding
Abstract
Visual distortions on images caused by bad weather conditions can have a negative impact on the performance of many outdoor vision systems. One often seen bad weather is rain which causes significant yet complex local intensity fluctuations in images. The paper aims at developing an effective algorithm to remove visual effects of rain from a single rainy image, i.e. separate the rain layer and the de-rained image layer from an rainy image. Built upon a non-linear generative model of rainy image, namely screen blend mode, we proposed a dictionary learning based algorithm for single image de-raining. The basic idea is to sparsely approximate the patches of two layers by very high discriminative codes over a learned dictionary with strong mutual exclusivity property. Such discriminative sparse codes lead to accurate separation of two layers from their non-linear composite. The experiments showed that the proposed method outperformed the existing single image de-raining methods on tested rain images.
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Citations
Cites
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892
Single image haze removal using dark channel prior
He, K., Sun, J., & Tang, X. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.168 · Cited by 7,601
Single image haze removal using dark channel prior
He, K., Sun, J., & Tang, X. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206515 · Cited by 2,414
Contrast restoration of weather degraded images
Narasimhan, S. G., & Nayar, S. K. (2003) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2003.1201821 · Cited by 1,642
Discriminative K-SVD for dictionary learning in face recognition
Zhang, Q., & Li, B. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539989 · Cited by 1,237
UCID: an uncompressed color image database:
Schaefer, G., & Stich, M. (2003) · SPIE Proceedings · doi:10.1117/12.525375 · Cited by 1,071
Image decomposition via the combination of sparse representations and a variational approach
Starck, J.-L., Elad, M., & Donoho, D. L. (2005) · IEEE Transactions on Image Processing · doi:10.1109/tip.2005.852206 · Cited by 987
Automatic Single-Image-Based Rain Streaks Removal via Image Decomposition
Kang, L.-W., Lin, C.-W., & Fu, Y.-H. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2179057 · Cited by 905
Learning a discriminative dictionary for sparse coding via label consistent K-SVD
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Detection and removal of rain from videos
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Cited by
Density-Aware Single Image De-raining Using a Multi-stream Dense Network
Zhang, H., & Patel, V. M. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00079 · Cited by 1,066
Clearing the Skies: A Deep Network Architecture for Single-Image Rain Removal
Fu, X., Huang, J., Ding, X., Liao, Y., & Paisley, J. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2691802 · Cited by 940
Erase or Fill? Deep Joint Recurrent Rain Removal and Reconstruction in Videos
Liu, J., Yang, W., Yang, S., & Guo, Z. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00341 · Cited by 193
Physics-Based Rendering for Improving Robustness to Rain
Halder, S., Lalonde, J.-F., & Charette, R. D. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.01030 · Cited by 130
Learning From Synthetic Photorealistic Raindrop for Single Image Raindrop Removal
Hao, Z., You, S., Li, Y., Li, K., & Lu, F. (2019) · 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) · doi:10.1109/iccvw.2019.00534 · Cited by 54
Metadata
- Authors
- Luo, Y., Xu, Y., & Ji, H.
- Venue
- 2015 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2015-12
- Pages
- 3397-3405
- OpenAlex
- W2209874411
- Cited by
- 837
- References
- 25
- FWCI
- 9.84
- Type
- conference paper
- Language
- en
- Open access
- closed
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