The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
A retinex-based enhancing approach for single underwater image
Abstract
Since the light is absorbed and scattered while traveling in water, color distortion, under-exposure and fuzz are three major problems of underwater imaging. In this paper, a novel retinex-based enhancing approach is proposed to enhance single underwater image. The proposed approach has mainly three steps to solve the problems mentioned above. First, a simple but effective color correction strategy is adopted to address the color distortion. Second, a variational framework for retinex is proposed to decompose the reflectance and the illumination, which represent the detail and brightness respectively, from single underwater image. An effective alternating direction optimization strategy is adopted to solve the proposed model. Third, the reflectance and the illumination are enhanced by different strategies to address the under-exposure and fuzz problem. The final enhanced image is obtained by combining use the enhanced reflectance and illumination. The enhanced result is improved by color correction, lightens dark regions, naturalness preservation, and well enhanced edges and details. Moreover, the proposed approach is a general method that can enhance other kinds of degraded image, such as sandstorm image.
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Citations
Cites
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 · cited 1×
“Since SCR is color corrected, the proposed variational retinex model is established on the luminance layer of Lab color space based on a large amount of psychophysical data concerning color-matching experiments performed by human observers [13][14].”
MethodContrast Limited Adaptive Histogram Equalization
Zuiderveld, K. (1994) · Graphics Gems · doi:10.1016/b978-0-12-336156-1.50061-6 · Cited by 4,192
Cited by
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
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
Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual Perception
Jahidul Islam, M., Luo, P., & Sattar, J. (2020) · Robotics: Science and Systems XVI · doi:10.15607/rss.2020.xvi.018 · Cited by 269
Metadata
- Authors
- Fu, X., Zhuang, P., Huang, Y., Liao, Y., Zhang, X.-P., & Ding, X.
- Venue
- 2014 IEEE International Conference on Image Processing (ICIP)
- Publisher
- IEEE
- Published
- 2014-10
- Pages
- 4572-4576
- ISSN
- 1522-4880 (ISSN-L)
- OpenAlex
- W2055447163
- Cited by
- 573
- References
- 2
- FWCI
- 4.32
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Fu, X., Zhuang, P., Huang, Y., Liao, Y., Zhang, X.-P., & Ding, X. (2014). A retinex-based enhancing approach for single underwater image. 2014 IEEE International Conference on Image Processing (ICIP). https://doi.org/10.1109/icip.2014.7025927
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