The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Single Image Layer Separation Using Relative Smoothness
Abstract
This paper addresses extracting two layers from an image where one layer is smoother than the other. This problem arises most notably in intrinsic image decomposition and reflection interference removal. Layer decomposition from a single-image is inherently ill-posed and solutions require additional constraints to be enforced. We introduce a novel strategy that regularizes the gradients of the two layers such that one has a long tail distribution and the other a short tail distribution. While imposing the long tail distribution is a common practice, our introduction of the short tail distribution on the second layer is unique. We formulate our problem in a probabilistic framework and describe an optimization scheme to solve this regularization with only a few iterations. We apply our approach to the intrinsic image and reflection removal problems and demonstrate high quality layer separation on par with other techniques but being significantly faster than prevailing methods.
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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
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A New Alternating Minimization Algorithm for Total Variation Image Reconstruction
Wang, Y., Yang, J., Yin, W., & Zhang, Y. (2008) · SIAM Journal on Imaging Sciences · doi:10.1137/080724265 · Cited by 1,975
Removing camera shake from a single photograph
Fergus, R., Singh, B., Hertzmann, A., Roweis, S. T., & Freeman, W. T. (2006) · ACM Transactions on Graphics · doi:10.1145/1141911.1141956 · Cited by 1,815
Unnatural L0 Sparse Representation for Natural Image Deblurring
Xu, L., Zheng, S., & Jia, J. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.147 · Cited by 1,079
Nonlinear image recovery with half-quadratic regularization
Geman, D., & Yang, C. (1995) · IEEE Transactions on Image Processing · doi:10.1109/83.392335 · Cited by 1,000
RECOVERING INTRINSIC SCENE CHARACTERISTICS FROM IMAGES
Barrow, H. G., & Tenenbaum, J. M. (1978) · Cited by 725
Deriving intrinsic images from image sequences
Weiss, Y. (2002) · Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 · doi:10.1109/iccv.2001.937606 · Cited by 573
Ground truth dataset and baseline evaluations for intrinsic image algorithms
Grosse, R., Johnson, M. K., Adelson, E. H., & Freeman, W. T. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459428 · Cited by 426
Recovering intrinsic images from a single image
Tappen, M. F., Freeman, W. T., & Adelson, E. H. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.185 · Cited by 423
Cited by
LIME: Low-Light Image Enhancement via Illumination Map Estimation
Guo, X., Li, Y., & Ling, H. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2639450 · Cited by 2,998
Self-citationStructure-Revealing Low-Light Image Enhancement Via Robust Retinex Model
Li, M., Liu, J., Yang, W., Sun, X., & Guo, Z. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2810539 · Cited by 1,245
Underexposed Photo Enhancement Using Deep Illumination Estimation
Wang, R., Zhang, Q., Fu, C.-W., Shen, X., Zheng, W.-S., & Jia, J. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00701 · Cited by 983
Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks
Liu, Y., Ma, X., Bailey, J., & Lu, F. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58607-2_11 · Cited by 531
Low-Light Image Enhancement via a Deep Hybrid Network
Ren, W., Liu, S., Ma, L., Xu, Q., Xu, X., Cao, X., Du, J., & Yang, M.-H. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2910412 · Cited by 508
Metadata
- Authors
- Li, Y., & Brown, M. S.
- Venue
- 2014 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2014-06
- Pages
- 2752-2759
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1980212291
- Cited by
- 354
- References
- 17
- FWCI
- 12.43
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Li, Y., & Brown, M. S. (2014). Single Image Layer Separation Using Relative Smoothness. 2014 IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2014.346
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
