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Underwater Image Enhancement by Wavelength Compensation and Dehazing
Abstract
Light scattering and color change are two major sources of distortion for underwater photography. Light scattering is caused by light incident on objects reflected and deflected multiple times by particles present in the water before reaching the camera. This in turn lowers the visibility and contrast of the image captured. Color change corresponds to the varying degrees of attenuation encountered by light traveling in the water with different wavelengths, rendering ambient underwater environments dominated by a bluish tone. No existing underwater processing techniques can handle light scattering and color change distortions suffered by underwater images, and the possible presence of artificial lighting simultaneously. This paper proposes a novel systematic approach to enhance underwater images by a dehazing algorithm, to compensate the attenuation discrepancy along the propagation path, and to take the influence of the possible presence of an artifical light source into consideration. Once the depth map, i.e., distances between the objects and the camera, is estimated, the foreground and background within a scene are segmented. The light intensities of foreground and background are compared to determine whether an artificial light source is employed during the image capturing process. After compensating the effect of artifical light, the haze phenomenon and discrepancy in wavelength attenuation along the underwater propagation path to camera are corrected. Next, the water depth in the image scene is estimated according to the residual energy ratios of different color channels existing in the background light. Based on the amount of attenuation corresponding to each light wavelength, color change compensation is conducted to restore color balance. The performance of the proposed algorithm for wavelength compensation and image dehazing (WCID) is evaluated both objectively and subjectively by utilizing ground-truth color patches and video downloaded from the Youtube website. Both results demonstrate that images with significantly enhanced visibility and superior color fidelity are obtained by the WCID proposed.
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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
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
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
Fast Image Dehazing Method Based on Linear Transformation
Wang, W., Yuan, X., Wu, X., & Liu, Y. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2652069 · Cited by 319
Underwater vision enhancement technologies: a comprehensive review, challenges, and recent trends
Zhou, J., Yang, T., & Zhang, W. (2023) · Applied Intelligence · doi:10.1007/s10489-022-03767-y · Cited by 149
Metadata
- Authors
- Chiang, J. Y., & Chen, Y.-C.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2012-04
- Volume
- 21
- Issue
- 4
- Pages
- 1756-1769
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W1976263166
- Cited by
- 1,225
- References
- 0
- FWCI
- 13.46
- Type
- journal article
- Language
- en
- Open access
- closed
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