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Automatic Single-Image-Based Rain Streaks Removal via Image Decomposition
Abstract
Rain removal from a video is a challenging problem and has been recently investigated extensively. Nevertheless, the problem of rain removal from a single image was rarely studied in the literature, where no temporal information among successive images can be exploited, making the problem very challenging. In this paper, we propose a single-image-based rain removal framework via properly formulating rain removal as an image decomposition problem based on morphological component analysis. Instead of directly applying a conventional image decomposition technique, the proposed method first decomposes an image into the low- and high-frequency (HF) parts using a bilateral filter. The HF part is then decomposed into a "rain component" and a "nonrain component" by performing dictionary learning and sparse coding. As a result, the rain component can be successfully removed from the image while preserving most original image details. Experimental results demonstrate the efficacy of the proposed algorithm.
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Citations
Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
Donoho, D. L. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2006.871582 · Cited by 23,994
Donoho, D. L. (2004) · Cited by 16,703
Speeded-Up Robust Features (SURF)
Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2007.09.014 · Cited by 13,557
A model of saliency-based visual attention for rapid scene analysis
Itti, L., Koch, C., & Niebur, E. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.730558 · Cited by 11,271
$rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
Aharon, M., Elad, M., & Bruckstein, A. (2006) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2006.881199 · Cited by 9,451
Matching pursuits with time-frequency dictionaries
Mallat, S. G., & Zhang, Z. (1993) · IEEE Transactions on Signal Processing · doi:10.1109/78.258082 · Cited by 9,112
Bilateral Filtering for Gray and Color Images
Tomasi, C., & Manduchi, R. (2002) · Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271) · doi:10.1109/iccv.1998.710815 · Cited by 8,177
Emergence of simple-cell receptive field properties by learning a sparse code for natural images
Olshausen, B. A., & Field, D. J. (1996) · Nature · doi:10.1038/381607a0 · Cited by 5,694
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
Elad, M., & Aharon, M. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2006.881969 · Cited by 5,321
Image information and visual quality
Sheikh, H. R., & Bovik, A. C. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2005.859378 · Cited by 4,104
A Review of Image Denoising Algorithms, with a New One
Buades, A., Coll, B., & Morel, J. M. (2005) · Multiscale Modeling & Simulation · doi:10.1137/040616024 · Cited by 4,083
Locality-constrained Linear Coding for image classification
Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., & Gong, Y. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5540018 · Cited by 3,100
Online Learning for Matrix Factorization and Sparse Coding
Mairal, J., Bach, F., Ponce, J., & Sapiro, G. (2009) · arXiv (Cornell University) · doi:10.48550/arxiv.0908.0050 · Cited by 2,258
From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images
Bruckstein, A. M., Donoho, D. L., & Elad, M. (2009) · SIAM Review · doi:10.1137/060657704 · Cited by 2,183
Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing
Elad, M. (2010) · doi:10.1007/978-1-4419-7011-4 · Cited by 1,897
Sparse Representation for Color Image Restoration
Mairal, J., Elad, M., & Sapiro, G. (2008) · IEEE Transactions on Image Processing · doi:10.1109/tip.2007.911828 · Cited by 1,716
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
Classification using intersection kernel support vector machines is efficient
Maji, S., Berg, A. C., & Malik, J. (2008) · 2008 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2008.4587630 · Cited by 940
Task-Driven Dictionary Learning
Mairal, J., Bach, F., & Ponce, J. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.156 · Cited by 823
Duarte-Carvajalino, J. M., & Sapiro, G. (2009) · IEEE Transactions on Image Processing · doi:10.1109/tip.2009.2022459 · Cited by 571
Detection and removal of rain from videos
Garg, K., & Nayar, S. K. (2004) · Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. · doi:10.1109/cvpr.2004.1315077 · Cited by 515
Multiresolution Bilateral Filtering for Image Denoising
Zhang, M., & Gunturk, B. K. (2008) · IEEE Transactions on Image Processing · doi:10.1109/tip.2008.2006658 · Cited by 512
Garg, K., & Nayar, S. K. (2007) · International Journal of Computer Vision · doi:10.1007/s11263-006-0028-6 · Cited by 477
Analysis of Rain and Snow in Frequency Space
Barnum, P. C., Narasimhan, S., & Kanade, T. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-008-0200-2 · Cited by 356
Rain or Snow Detection in Image Sequences Through Use of a Histogram of Orientation of Streaks
Bossu, J., Hautière, N., & Tarel, J.-P. (2011) · International Journal of Computer Vision · doi:10.1007/s11263-011-0421-7 · Cited by 349
Morphological Component Analysis: An Adaptive Thresholding Strategy
Bobin, J., Starck, J.-L., Fadili, J. M., Moudden, Y., & Donoho, D. L. (2007) · IEEE Transactions on Image Processing · doi:10.1109/tip.2007.907073 · Cited by 314
Rain Removal in Video by Combining Temporal and Chromatic Properties
Zhang, X., Li, H., Qi, Y., Leow, W., & Ng, T. (2006) · 2006 IEEE International Conference on Multimedia and Expo · doi:10.1109/icme.2006.262572 · Cited by 293
Trainable classifier-fusion schemes: An application to pedestrian detection
Junior, O. L., Delgado, D., Goncalves, V., & Nunes, U. (2009) · 2009 12th International IEEE Conference on Intelligent Transportation Systems · doi:10.1109/itsc.2009.5309700 · Cited by 229
Photorealistic rendering of rain streaks
Garg, K., & Nayar, S. K. (2006) · ACM Transactions on Graphics · doi:10.1145/1141911.1141985 · Cited by 227
Garg, K., & Nayar, S. K. (2005) · Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 · doi:10.1109/iccv.2005.253 · Cited by 225
Image Decomposition and Separation Using Sparse Representations: An Overview
Fadili, M. J., Starck, J.-L., Bobin, J., & Moudden, Y. (2010) · Proceedings of the IEEE · doi:10.1109/jproc.2009.2024776 · Cited by 183
Factorized Latent Spaces with Structured Sparsity
Jia, Y., Salzmann, M., & Darrell, T. (2010) · Cited by 177
Video-based raindrop detection for improved image registration
Roser, M., & Geiger, A. (2009) · 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops · doi:10.1109/iccvw.2009.5457650 · Cited by 126
Using the Shape Characteristics of Rain to Identify and Remove Rain from Video
Brewer, N., & Liu, N. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-89689-0_49 · Cited by 120
Shehata, M., Cai, J., Badawy, W., Burr, T. W., Pervez, M. S., Johannesson, R. J., & Radmanesh, A. (2008) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2008.915644 · Cited by 109
Single-frame-based rain removal via image decomposition
Fu, Y.-H., Kang, L., Lin, C., et al. (2011) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2011.5946766 · Cited by 96
Self-citationHalimeh, J. C., & Roser, M. (2009) · 2009 IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2009.5164347 · Cited by 76
MCALab: Reproducible Research in Signal and Image Decomposition and Inpainting
Fadili, J. M., Starck, J., Elad, M., & Donoho, D. L. (2010) · Computing in Science & Engineering · doi:10.1109/mcse.2010.14 · Cited by 74
Dictionary learning-based distributed compressive video sensing
Chen, H.-W., Kang, L., & Lu, C.-S. (2010) · Picture Coding Symposium · doi:10.1109/pcs.2010.5702466 · Cited by 51
Self-citationLearning adapted dictionaries for geometry and texture separation
Gabriel Peyré, A., Fadili, J. M., & Starck, J. (2007) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.731244 · Cited by 34
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Removing Rain from Single Images via a Deep Detail Network
Fu, X., Huang, J., Zeng, D., Huang, Y., Ding, X., & Paisley, J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.186 · Cited by 1,235
Deep Joint Rain Detection and Removal from a Single Image
Yang, W., Tan, R. T., Feng, J., Liu, J., Guo, Z., & Yan, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.183 · Cited by 1,231
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
Recurrent Squeeze-and-Excitation Context Aggregation Net for Single Image Deraining
Li, X., Wu, J., Lin, Z., Liu, H., & Zha, H. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_16 · Cited by 927
Metadata
- Authors
- Kang, L.-W., Lin, C.-W., & Fu, Y.-H.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2012-04
- Volume
- 21
- Issue
- 4
- Pages
- 1742-1755
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W2121396509
- Cited by
- 905
- References
- 42
- FWCI
- 14.49
- Type
- journal article
- Language
- en
- Open access
- closed
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