The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Contrast restoration of weather degraded images
Abstract
Images of outdoor scenes captured in bad weather suffer from poor contrast. Under bad weather conditions, the light reaching a camera is severely scattered by the atmosphere. The resulting decay in contrast varies across the scene and is exponential in the depths of scene points. Therefore, traditional space invariant image processing techniques are not sufficient to remove weather effects from images. We present a physics-based model that describes the appearances of scenes in uniform bad weather conditions. Changes in intensities of scene points under different weather conditions provide simple constraints to detect depth discontinuities in the scene and also to compute scene structure. Then, a fast algorithm to restore scene contrast is presented. In contrast to previous techniques, our weather removal algorithm does not require any a priori scene structure, distributions of scene reflectances, or detailed knowledge about the particular weather condition. All the methods described in this paper are effective under a wide range of weather conditions including haze, mist, fog, and conditions arising due to other aerosols. Further, our methods can be applied to gray scale, RGB color, multispectral and even IR images. We also extend our techniques to restore contrast of scenes with moving objects, captured using a video camera.
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Citations
Cites
Chandrasekhar, S. (1950) · CERN Document Server (European Organization for Nuclear Research) · Cited by 8,702
Light Scattering by Small Particles
van de Hulst, H. C. (1981) · CERN Document Server (European Organization for Nuclear Research) · Cited by 7,938
Adaptive background mixture models for real-time tracking
Stauffer, C., & Grimson, W. E. L. (2003) · Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149) · doi:10.1109/cvpr.1999.784637 · Cited by 6,923
Light Scattering by Small Particles
Van de Hulst, H. C., & Twersky, V. (1957) · Physics Today · doi:10.1063/1.3060205 · Cited by 5,619
Light scattering by small particles
Snowdon, S. C. (1957) · Journal of the Franklin Institute · doi:10.1016/0016-0032(57)90988-2 · Cited by 3,716
Narasimhan, S. G., & Nayar, S. K. (2002) · International Journal of Computer Vision · doi:10.1023/a:1016328200723 · Cited by 1,720
Self-citationUsing color to separate reflection components
Shafer, S. A. (1985) · Color Research & Application · doi:10.1002/col.5080100409 · Cited by 1,275
Nayar, S. K., & Narasimhan, S. G. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790306 · Cited by 883
Self-citationInstant dehazing of images using polarization
Schechner, Y. Y., Narasimhan, S. G., & Nayar, S. K. (2005) · Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001 · doi:10.1109/cvpr.2001.990493 · Cited by 859
Self-citationChromatic framework for vision in bad weather
Narasimhan, S. G., & Nayar, S. K. (2002) · Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662) · doi:10.1109/cvpr.2000.855874 · Cited by 786
Self-citation
Cited by
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
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
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
An Underwater Image Enhancement Benchmark Dataset and Beyond
Li, C., Guo, C., Ren, W., Cong, R., Hou, J., Kwong, S., & Tao, D. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2955241 · Cited by 2,373
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
Metadata
- Authors
- Narasimhan, S. G., & Nayar, S. K.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2003-06
- Volume
- 25
- Issue
- 6
- Pages
- 713-724
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2097900287
- Cited by
- 1,642
- References
- 21
- FWCI
- 6.19
- Type
- journal article
- Language
- en
- Open access
- closed
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