Underwater image enhancement by double compensation with comparative adjustment or edge reinforcement

Abstract

The phenomenon of attenuation and scattering of light propagating in water leads to such problems as color deviation and blur in underwater imaging. These problems bring great challenges to the subsequent feature matching, target recognition and other tasks. Therefore, this paper proposes an underwater image enhancement method by double compensation with comparative adjustment or edge reinforcement. The experiments have found that the proposed method has good underwater color image quality evaluation (UCIQE) value, underwater image quality measures (UIQM) value, and the number of feature matching points. This demonstrates that the proposed method has good color correction ability for underwater images with different attenuation levels, where the processed images have more details suitable for feature matching.

Keywords

A

References

[1] Zhou J C, Wang Y Y, Zhang W S, et al.. Underwater image restoration via feature priors to estimate background light and optimized transmission map. Optics express, 2021, 29(18): 28228-28245 J]

[2] Berman D, Levy D, Avidan S. Underwater single image color restoration using haze-lines and a new quantitative dataset. IEEE transactions on pattern analysis and machine intelligence, 2021, 43(8): 2822-2837[J]

[3] Land E H, Cann M J. Lightness and retinex theory. Journal of the Optical Society of America, 1971, 61(1): 1-11 J]

[4] Zhu X D, Lin M X, Zhao M Y. Adaptive underwater image enhancement based on color compensation and fusion. Signal, image and video processing, 2023, 17(5): 2201-2210 J]

[5] Ancuti C O, Ancuti C, Vleeschouwer C. Color balance and fusion for underwater image enhancement. IEEE transactions on image processing, 2018, 27: 379-393 J]

[6] An S, Xu L. HFM: a hybrid fusion method for underwater image enhancement. Engineering applications of artificial intelligence, 2024, 127: 107219 J]

[7] Yuan J Y, Cai Z C, Cao W. TEBCF: real-world underwater image texture enhancement model based on blurriness and color fusion. IEEE transactions on geoscience and remote sensing, 2022, 60: 1-15[J]

[8] Zhang W, Zhou L, Zhuang P X. Underwater image enhancement via weighted wavelet visual perception fusion. IEEE transactions on circuits and systems for video technology, 2024, 34(4): 2469-2483 J]

[9] Fu X, Fan Z, Ling M. Two-step approach for single underwater image enhancement. 2017 International Symposium on Intelligent Signal Processing and Communication Systems, November 6–9, 2017, Xiamen, China, 2017, New York, IEEE789794[C]

[10] Hou G, Pan Z, Huang B. Hue preserving-based approach for underwater colour image enhancement. IET image processing, 2018, 12(2): 292-298 J]

[11] Zhang W, Zhuang P, Sun H H. Underwater image enhancement via minimal color loss and locally adaptive contrast enhancement. IEEE transactions on image processing, 2022, 31: 3997-4010 J]

[12] Yan X, Wang G, Lin P. Underwater image dehazing using a novel color channel based dual transmission map estimation. Multimedia tools and applications, 2024, 83(7): 20169-20192 J]

[13] Liang Z, Ding X, Wang Y. Generalization of underwater dark channel prior for underwater image restoration. IEEE transactions on circuits and systems for video technology, 2022, 32(7): 4879-4884 J]

[14] Zhang J K, Liu H. Coordinated underwater dark channel prior for alleviating halos and patch artifacts of challenging image enhancement. IEEE International Workshop on Multimedia Signal Processing, September 28, 2022, Shanghai, China, 2022, New York, IEEE16[C]

[15] He K M, Sun J, Tang X O. Single image haze removal using dark channel prior. IEEE Conference on Computer Vision and Pattern Recognition, June 20–25, 2009, Miami Beach, Florida, USA, 2009, New York, IEEE19561963[C]

[16] Deluxni N, Sudhakaran P, Kitm O. A review on image enhancement and restoration techniques for underwater optical imaging applications. IEEE access, 2023, 11: 111715-111737 J]

[17] Huang D M, Wang Y, Song W. Underwater image enhancement method using adaptive histogram stretching in different color models. Journal of image and graphics, 2018, 23(5): 640-651[J]

[18] Basha M S, Ramakrishnan M. Color image enhancement based on modified contrast limited adaptive histogram equalization. International journal of engineering research & technology, 2013, 2(12): 3083-3088[J]

[19] Ansari S. A review on SIFT and SURF for underwater image feature detection and matching. 2019 IEEE International Conference on Electrical, Computer and Communication Technologies, February 20–22, 2019, Coimbatore, India, 2019, New York, IEEE14[C]

[20] Zhang K, Jin W, Qiu S. Multi-scale retinex enhancement algorithm on luminance channel of color underwater image. Infrared technology, 2011, 33(11): 630-634[J]

[21] Li C Y, Guo C L, Ren W Q. An underwater image enhancement benchmark dataset and beyond. IEEE transactions on image processing, 2020, 29: 4376-4389 J]

[22] Porto M T, Branzan A A, Hoeberechts M. A contrast-guided approach for the enhancement of low-lighting underwater images. Journal of imaging, 2019, 5(10): 79-103 J]

[23] Yang M, Sowmya A. An underwater color image quality evaluation metric. IEEE transactions on image processing, 2015, 24(12): 6062-6071 J]

[24] Panetta K, Gao C, Agaian S. Human-visual-system-inspired underwater image quality measures. IEEE journal of oceanic engineering, 2016, 41(3): 541-551 J]

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Tianjin University of Technology