The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Fast visibility restoration from a single color or gray level image
Abstract
One source of difficulties when processing outdoor images is the presence of haze, fog or smoke which fades the colors and reduces the contrast of the observed objects. We introduce a novel algorithm and variants for visibility restoration from a single image. The main advantage of the proposed algorithm compared with other is its speed: its complexity is a linear function of the number of image pixels only. This speed allows visibility restoration to be applied for the first time within real-time processing applications such as sign, lane-marking and obstacle detection from an in-vehicle camera. Another advantage is the possibility to handle both color images or gray level images since the ambiguity between the presence of fog and the objects with low color saturation is solved by assuming only small objects can have colors with low saturation. The algorithm is controlled only by a few parameters and consists in: atmospheric veil inference, image restoration and smoothing, tone mapping. A comparative study and quantitative evaluation is proposed with a few other state of the art algorithms which demonstrates that similar or better quality results are obtained. Finally, an application is presented to lane-marking extraction in gray level images, illustrating the interest of the approach.
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Citations
Cites
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
Visibility in bad weather from a single image
Tan, R. T. (2008) · 2008 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2008.4587643 · Cited by 2,280
Fattal, R. (2008) · ACM Transactions on Graphics · doi:10.1145/1360612.1360671 · Cited by 2,114
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
Photographic tone reproduction for digital images
Reinhard, E., Stark, M., Shirley, P., & Ferwerda, J. (2002) · Proceedings of the 29th annual conference on Computer graphics and interactive techniques · doi:10.1145/566570.566575 · Cited by 1,408
BLIND CONTRAST ENHANCEMENT ASSESSMENT BY GRADIENT RATIOING AT VISIBLE EDGES
Hautière, N., Tarel, J.-P., Aubert, D., & Dumont, É. (2011) · Image Analysis & Stereology · doi:10.5566/ias.v27.p87-95 · Cited by 742
Self-citationDeep photo: model-based photograph enhancement and viewing
Kopf, J., Neubert, B., Chen, B., Cohen, M., Cohen-Or, D., Deussen, O., Uyttendaele, M., & Lischinski, D. (2008) · ACM Transactions on Graphics · doi:10.1145/1409060.1409069 · Cited by 713
Shwartz, S., Namer, E., & Schechner, Y. Y. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.71 · Cited by 512
Fattal, R. (2008) · ACM SIGGRAPH 2008 papers · doi:10.1145/1399504.1360671 · Cited by 374
Cited by
DehazeNet: An End-to-End System for Single Image Haze Removal
Cai, B., Xu, X., Jia, K., Qing, C., & Tao, D. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2598681 · Cited by 3,367
Berman, D., Treibitz, T., & Avidan, S. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.185 · Cited by 1,642
Color balance and fusion for underwater image enhancement
Ancuti, C. O., Ancuti, C., De Vleeschouwer, C., & Bekaert, P. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2759252 · Cited by 1,357
Structure-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
Automatic Red-Channel underwater image restoration
Galdran, A., Pardo, D., Picón, A., & Alvarez-Gila, A. (2015) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2014.11.006 · Cited by 995
Metadata
- Authors
- Tarel, J.-P., & Hautiere, N.
- Venue
- 2009 IEEE 12th International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 2009-09
- Pages
- 2201-2208
- OpenAlex
- W2536722097
- Cited by
- 1,313
- References
- 15
- FWCI
- 20.45
- Type
- conference paper
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
