The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.
Underwater image processing and analysis: A review
Abstract
Abstract With increasing attentions being drawn to the underwater observation and utilization of marine resources in recent years, underwater image processing and analysis have become an active research hotspot. Different from the general images, marine environment is usually faced with some complicated situations such as underwater turbulence and diffusion, severe absorption and scattering of water body, various noises, low contrast, uniform illumination, monotonous color, complex underwater-background. In response to these typical challenges, a large body of works in underwater image processing has been exploited in recent years. This survey introduces a review of existing relatively mature and representative underwater image processing models, which are classified into seven categories including enhancement, fog removal, noise reduction, segmentation, salient object detection, color constancy and restoration. We then objectively evaluate the current situations and future development trend of underwater image processing, and provide some insights into the prospective research directions to promote the development of underwater vision and beyond.
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Citations
Cites
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
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 by Wavelength Compensation and Dehazing
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Real-World Underwater Enhancement: Challenges, Benchmarks, and Solutions Under Natural Light
Liu, R., Fan, X., Zhu, M., Hou, M., & Luo, Z. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2019.2963772 · Cited by 863
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2013 IEEE International Conference on Image Processing
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Cited by
A systematic review and analysis of deep learning-based underwater object detection
Xu, S., Zhang, M., Song, W., Mei, H., He, Q., & Liotta, A. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.01.056 · Cited by 319
Metadata
- Authors
- Jian, M., Liu, X., Luo, H., Lu, X., Yu, H., & Dong, J.
- Venue
- Signal Processing: Image Communication
- Publisher
- Elsevier BV
- Published
- 2021-02
- Volume
- 91
- Pages
- 116088
- ISSN
- 0923-5965 (ISSN-L)
- OpenAlex
- W3106850582
- Cited by
- 269
- References
- 57
- FWCI
- 8.47
- Type
- journal article
- Language
- en
- Open access
- closed
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