ABSTRACT
Due to the unique image formation principle and the limitations of imaging devices, underwater images usually suffer from low contrast, color degradation, and blurring effects, which seriously hinder the interpretation of the image content. Additionally, several underwater image enhancement and restoration (UIER) algorithms have been proposed to improve the quality of underwater images. However, their performances vary greatly under different underwater scenarios. Therefore, establishing an effective quality evaluation metric plays an irreplaceable role in assessing the underwater image quality as well as evaluating the performances of UIER algorithms. In this paper, we propose a novel training-based blind underwater image quality elevation metric. Technically, the proposed metric extracts and fuses three groups of features covering naturalness, color, and contrast. Moreover, observing that the severity of color cast is highly related to the chroma of the image, we develop an underwater color factor feature to accurately estimate the severity of color distortion. Experimental results demonstrate the superior performance of the proposed method and its ability to evaluate the performance of UIER algorithms.
- Baoxiang Huang, Linyao Ge, Xiaoyan Chen, Ge Chen. 2021. Vertical structure-based classification of oceanic eddy using 3-d convolutional neural network. IEEE Trans. Geosci. Remote. Sens. 60 (Aug. 2021), 1-14.Google Scholar
- Domenico D. Bloisi, Fabio Previtali, Andrea Pennisi, Daniele Nardi, Michele Fiorini. 2017. Enhancing automatic maritime surveillance systems with visual information. IEEE Trans. Intell. Transp. Syst. 18, 4 (Apr. 2017), 824–833.Google ScholarDigital Library
- Chun Lung Philip Chen, Jin Zhou, Wei Zhao. 2012. A real-time vehicle navigation algorithm in sensor network environments. IEEE Trans. Intell. Transp. Syst. 13, 4 (Dec. 2012), 1657–1666.Google Scholar
- Dorian Cazau, Julien Bonnel, Mark Baumgartner. 2018. Wind speed estimation using acoustic underwater glider in a near-shore marine environment. IEEE Trans. Geosci. Remote Sens. 57, 4 (Nov. 2018), 2097–2106.Google Scholar
- Jules S. Jaffe. 1990. Computer modeling and the design of optimal underwater imaging systems. IEEE J. Ocean. Eng. 15, 2 (Apr. 1990), 101–111.Google ScholarCross Ref
- Nan Li, Guojia Hou, Yuhai Liu, Zhenkan Pan, Lu Tan. 2020 Single underwater image enhancement using integrated variational model. Digit. Signal Process. 2022, 129 (Sep. 2022), 103660.Google Scholar
- Qi Qi, Yongchang Zhang, Fei Tian, Qingming Jonathan Wu, Kunqian Li, Xin Luan, Dalei Song. 2022. Underwater image co-enhancement with correlation feature matching and joint learning. IEEE Trans. Circuits Syst. Video Technol. 32, 3 (Mar. 2022), 1133-1147.Google ScholarCross Ref
- Guojia Hou, Xin Zhao, Zhenkuan Pan, Huan Yang, Lu Tan, Jingming Li. 2020. Benchmarking underwater image enhancement and restoration, and beyond. IEEE Access 8 (Jul. 2020), pp. 122078-122091.Google Scholar
- Jun Xie, Guojia Hou, Guodong Wang, Zhenkuan Pan. 2022. A variational framework for underwater image dehazing and deblurring. IEEE Trans. Circuits Syst. Video Technol. 32, 6 (Jun. 2022), 3514-3526.Google ScholarCross Ref
- Anish Mittal, Anush Krishna Moorthy, Alan Conrad Bovik. 2012. No-reference image quality assessment in the spatial domain. IEEE Trans. Image Process. 21, 12 (Dec. 2012), 4695-4708Google ScholarDigital Library
- Anish Mittal, Rajiv Soundararajan, Alan Conrad Bovik. 2013. Making a “completely blind” image quality analyzer. IEEE Signal Process. Lett. 20, 3 (Mar. 2013), 209-212.Google ScholarCross Ref
- Lark Kwon Choi, Jaehee You, Alan Conrad Bovik. 2015. Referenceless prediction of perceptual fog density and perceptual image defogging. IEEE Trans. Image Process. 24, 11 (Nov. 2015), 3888-3901.Google Scholar
- Miao Yang and Arcot Sowmya. 2015. An underwater color image quality evaluation metric. IEEE Trans. Image Process. 24, 12 (Dec. 2015) 6062-6071.Google ScholarDigital Library
- Karen Panetta, Chen Gao, and Sos Again. 2016. Human-visual-system-inspired underwater image quality measures. IEEE J. Ocean. Eng. 41, 3 (Jul. 2016), 541-551.Google ScholarCross Ref
- Yan Wang, Na Li, Zongying Li, Zhaorui Gu, Haiyong Zheng, Bing Zheng, Mengnan Sun. 2017. An imaging-inspired no-reference underwater color image quality assessment metric. Comput. Electron. Eng. 70 (Dec. 2017), 904-913.Google Scholar
- Ning Yang, Qihang Zhong, Kun Li, Runmin Cong, Yao Zhao, Sam Kwong. A reference-free underwater image quality assessment metric in frequency domain. Signal Process. Image Commun. 94 (Mar. 2021), 116218.Google Scholar
- Daniel L Ruderman. 1994. The statistics of natural images. Netw., Comput. Neural Syst. 5, 4 (Apr. 1994), 517-548.Google ScholarCross Ref
- Lin Zhang, Lei Zhang, Alan Conrad Bovik. A feature-enriched completely blind image quality evaluator. IEEE Trans. Image Process. 24, 9 (Apr. 2015), 2579 – 2591.Google Scholar
- Krešimir Matković, László Neumann, Attila Neumann, Thomas Psik, Werner Purgathofer. 2005. Global contrast factor - a new approach to image contrast. In Proceedings of the First Eurographics conference on Computational Aesthetics in Graphics, Visualization and Imaging. (May. 2005), 159–167.Google ScholarDigital Library
- Alan Conrad Bovik, Muhammad Farooq Sabir, Hamid R. Sheikh. 2006. A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Trans. Image Process. 15, 11(Nov. 2006), 3440-3451.Google Scholar
- Yan-Tsung Peng, Pamela Cosman. 2017. Underwater image restoration based on image blurriness and light absorption. IEEE Trans. Image Process. 26, 4 (Apr. 2017), 1579-1594.Google ScholarDigital Library
- Codruta Orniana Ancuti, Cosmin Ancuti, Christophe De Vleeschouwer, Philippe Bekaert. 2018. Color balance and fusion for underwater image enhancement. IEEE Trans. Image Process. 27, 1 (Jan. 2018), 379-393.Google ScholarCross Ref
- Xinjie Li, Guojia Hou, Kunqian Li, Zhenkuan Pan. Enhancing underwater image via adaptive color and contrast enhancement, and denoising. Eng. Appl. Artif. Intel. 111 (May 2022), 104759.Google Scholar
Recommendations
UID2021: An Underwater Image Dataset for Evaluation of No-Reference Quality Assessment Metrics
Achieving subjective and objective quality assessment of underwater images is of high significance in underwater visual perception and image/video processing. However, the development of underwater image quality assessment (UIQA) is limited for the lack ...
An Underwater Color Image Quality Evaluation Metric
Quality evaluation of underwater images is a key goal of underwater video image retrieval and intelligent processing. To date, no metric has been proposed for underwater color image quality evaluation (UCIQE). The special absorption and scattering ...
An Underwater Color Image Enhancement Model Based on Comprehensive Processing
ICNSER2020: Proceedings of the 2nd International Conference on Industrial Control Network And System Engineering ResearchThe propagation of light in water is affected by the absorption of water and the scattering of particles in water, which leads to the problems of low contrast, blur, color distortion and so on, the traditional underwater image enhancement algorithm is ...
Comments