The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Underwater Image Restoration Based on Image Blurriness and Light Absorption
Abstract
Underwater images often suffer from color distortion and low contrast, because light is scattered and absorbed when traveling through water. Such images with different color tones can be shot in various lighting conditions, making restoration and enhancement difficult. We propose a depth estimation method for underwater scenes based on image blurriness and light absorption, which can be used in the image formation model (IFM) to restore and enhance underwater images. Previous IFM-based image restoration methods estimate scene depth based on the dark channel prior or the maximum intensity prior. These are frequently invalidated by the lighting conditions in underwater images, leading to poor restoration results. The proposed method estimates underwater scene depth more accurately. Experimental results on restoring real and synthesized underwater images demonstrate that the proposed method outperforms other IFM-based underwater image restoration methods.
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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
No-Reference Image Quality Assessment in the Spatial Domain
Mittal, A., Moorthy, A. K., & Bovik, A. C. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2214050 · Cited by 6,064
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Contrast 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
Underwater scene prior inspired deep underwater image and video enhancement
Li, C., Anwar, S., & Porikli, F. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2019.107038 · Cited by 1,252
Underwater Image Enhancement Using a Multiscale Dense Generative Adversarial Network
Guo, Y., Li, H., & Zhuang, P. (2020) · IEEE Journal of Oceanic Engineering · doi:10.1109/joe.2019.2911447 · Cited by 508
A Variational Framework for Underwater Image Dehazing and Deblurring
Xie, J., Hou, G., Wang, G., & Pan, Z. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3115791 · Cited by 259
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
- Peng, Y.-T., & Cosman, P. C.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2017-04
- Volume
- 26
- Issue
- 4
- Pages
- 1579-1594
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W2587107113
- Cited by
- 1,250
- References
- 4
- FWCI
- 18.76
- Type
- journal article
- Language
- en
- Open access
- closed
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