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收稿：2021-04-30，
修回：2021-8-10，
录用：2021-8-17，
纸质出版：2022-05-16
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目的
2
由于光在水中的衰减/散射以及微生物对光的吸收/反射等影响，水下图像通常存在色偏、模糊、光照不均匀以及对比度过低等诸多质量问题。研究人员对此提出了许多不同的水下图像增强算法。为了探究目前已有的水下图像增强算法的性能和图像质量客观评价方法是否适用于评估水下图像，本文开展大规模主观实验来对比不同水下图像增强算法在真实水下图像数据集上的性能，并对现有图像质量评价方法用于评估水下图像的准确性进行测试。
方法
2
构建了一个真实的水下图像数据集，其中包含100幅原始水下图像以及对应的1 000幅由10种主流水下图像增强方法增强后的图像。基于成对比较的策略开展水下图像主观质量评价，进一步对主观评价得到的结果进行分析，包括一致性分析、收敛性分析以及显著性检验。最后将10种现有主流的无参考图像质量评价在本文数据集上进行测试，检验其在真实水下图像数据集上的评价性能。
结果
2
一致性分析中，该数据集包含的主观评分有较高的肯德尔一致性系数，其值为0.41；收敛性分析中，所收集的投票数量与图像数量足够得到稳定的主观评分；表明本文构建的数据集具有良好的有效性与可靠性。此外，目前对比自然图像的无参考图像质量评价方法并不适用于水下图像数据集，验证了水下图像与自然图像的巨大差异。
结论
2
本文构建的真实水下图像数据集为未来水下图像质量客观评价方法以及水下图像增强算法的研究提供了参考与支持。所涉及的图像以及所有收集的用户数据，都在项目主页(
https://github.com/yia-yuese/RealUWIQ-dataset
https://github.com/yia-yuese/RealUWIQ-dataset
)上公开。
Objective
2
Underwater images processing are essential to marine issues in the context of defense
environmental protection and engineering. However
there are always severe quality degradation issues like color cast
blur
and low contrast are greatly restricted the quality of underwater imaging and operation systems due to the inner-water light attenuation/scattering and the microbe derived absorption/reflection of light. Underwater image enhancement (UIE) algorithms have been demonstrated to improve the quality of underwater images nowadays. The two aspects of challenges are critical to be illustrated as below: one of huge gap between the synthesized and in-situ underwater images processing are constrained of complicated degradations of diverse underwater environments. The other is challenged that the existing objective image quality metrics are matched to evaluate the in-situ quality of various UIE algorithms. To resolve the above two issues
our demonstration has illustrated as following: first
we build up a real-world underwater image quality evaluation dataset to compare the performance of different UIE algorithms based on a collection of in-situ underwater images. Next
we evaluate the performance of existing image quality evaluation metrics on our generated dataset.
Method
2
First
we collect 100 real-world underwater images
including 60 color cast-dominant and 40 blur-dominant ones
and apply 10 representative UIE algorithms to enhance the 100 raw underwater images. A total number of 1 000 enhanced results (10 results for each raw underwater image) are generated. Next
we conduct complex human subjective studies to evaluate the performance of different UIE algorithms based on the pairwise comparison (PC) strategy. Thirdly
we analyze the results obtained from our subjective studies to demonstrate the reliability of our human subjective studies and get insights on the pros and cons of each UIE algorithm. The Bradley-Terry (B-T) model on the PC results obtained B-T scores as the ground truth quality scores of the enhanced underwater images. Finally
we test the capabilities of existing image quality metrics via the correlations between the B-T scores and the predicted 10 existing representative no-reference image quality metrics for evaluating UIE results.
Result
2
We illustrates the Kendall coefficient of inner subjects' protocols
a convergence analysis and conducts a significance test to verify the dataset. The Kendall coefficient of inner subjects' protocols on the full-set is around 0.41
which demonstrates a qualified inter-subject consistency level. Such coefficient is slightly different on the two subsets
i.e.
0.39 and 0.44 on the color cast subset and blur subset
respectively. In respect of the convergence analysis
the mean and the variance of each underwater image enhancement algorithms tend to be clarified in the context of the increasing of the number of votes and the number of images.The similar subjective scores are obtained for each underwater enhancement algorithms. The significance for test results demonstrates that GL-Net is the best and underwater image enhancement convolutional neural network(UWCNN) is the worst for the adopted 10 UIE algorithms. In addition
there is slight difference on the performance rankings of different UIE algorithms on the two subsets. Finally
an existing no-reference image quality metrics can be unqualified for UIE algorithms evaluation.
Conclusion
2
Our first contribution is based on an in-situ underwater image quality evaluation dataset through conducting human subjective studies to compare the performance of various UIE algorithms with a collection of in-situ underwater images. The other one is that the performance of existing image quality evaluation methods is evaluated based on our dataset and the limitation of the existing image quality metrics is identified for UIE quality evaluation. Overall
this research targets underwater image quality evaluation metrics. All the images and collected data involved are available at:
https://github.com/yia-yuese/RealUWIQ-dataset
https://github.com/yia-yuese/RealUWIQ-dataset
.
Akkaynak D and Treibitz D. 2018. A revised underwater image formation model//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE: 6723-6732 [ DOI: 10.1109/CVPR.2018.00703 http://dx.doi.org/10.1109/CVPR.2018.00703 ]
Akkaynak D, Treibitz T, Shlesinger T, Loya R, Tamir R and Iluz D. 2017. What is the space of attenuation coefficients in underwater computer vision?//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, USA: IEEE: 568-577 [ DOI: 10.1109/CVPR.2017.68 http://dx.doi.org/10.1109/CVPR.2017.68 ]
Bradley R A and Terry M E. 1952. Rank analysis of incomplete block designs: the method of paired comparisons. Biometrika, 39(3/4): 324-345 [DOI: 10.1093/biomet/39.3-4.324]
Drews P L J, Nascimento E R, Botelho S S C and Campos M F M. 2016. Underwater depth estimation and image restoration based on single images. IEEE Computer Graphics and Applications, 36(2): 24-35 [DOI: 10.1109/MCG.2016.26]
Fisher R A. 1926. Statistical methods for research workers. Journal of the Royal Statistical Society, 89(1): 144-145 [DOI: 10.2307/2341488]
Fu X Y and Cao X Y. 2020. Underwater image enhancement with global-local networks and compressed-histogram equalization. Signal Processing: Image Communication, 86: #115892 [DOI: 10.1016/j.image.2020.115892]
Fu X Y, Fan Z W, Ling M, Huang Y and Ding X H. 2017. Two-step approach for single underwater image enhancement//Proceedings of 2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS). Xiamen, China: IEEE: 789-794 [ DOI: 10.1109/ISPACS.2017.8266583 http://dx.doi.org/10.1109/ISPACS.2017.8266583 ]
Fu X Y, Zhuang P X, Huang Y, Liao Y H, Zhang X P and Ding X H. 2014. A retinex-based enhancing approach for single underwater image//Proceedings of 2014 IEEE International Conference on Image Processing (ICIP). Paris, France: IEEE: 4572-4576 [ DOI: 10.1109/ICIP.2014.7025927 http://dx.doi.org/10.1109/ICIP.2014.7025927 ]
Ghadiyaram D and Bovik A C. 2017. Perceptual quality prediction on authentically distorted images using a bag of features approach. Journal of Vision, 17(1): #32 [DOI: 10.1167/17.1.32]
Ghani A S A and Isa N A M. 2014. Underwater image quality enhancement through composition of dual-intensity images and Rayleigh-stretching//Proceedings of the 4th IEEE International Conference on Consumer Electronics Berlin (ICCE-Berlin). Berlin, Germany: IEEE: 219-220 [ DOI: 10.1109/ICCE-Berlin.2014.7034265 http://dx.doi.org/10.1109/ICCE-Berlin.2014.7034265 ]
Gilbert E N. 1964. Review: H. A. David, the method of paired comparisons. The Annals of Mathematical Statistics, 35(3): 1386-1387 [DOI: 10.1214/aoms/1177703303]
Hou W L, Woods S, Jarosz E, Goode W and Weidemann A. 2012. Optical turbulence on underwater image degradation in natural environments. Applied Optics, 51(14): 2678-2686 [DOI: 10.1364/AO.51.002678]
Huang D M, Wang Y, Song W, Sequeira J and Mavromatis S. 2018. Shallow-water image enhancement using relative global histogram stretching based on adaptive parameter acquisition//Proceedings of the 24th International Conference on Multimedia Modeling. Bangkok, Thailand: Springer: 453-465 [ DOI: 10.1007/978-3-319-73603-7_37 http://dx.doi.org/10.1007/978-3-319-73603-7_37 ]
Jaffe J S. 1990. Computer modeling and the design of optimal underwater imaging systems. IEEE Journal of Oceanic Engineering, 15(2): 101-111 [DOI: 10.1109/48.50695]
Jaffe J S. 2015. Underwater optical imaging: the past, the present, and the prospects. IEEE Journal of Oceanic Engineering, 40(3): 683-700 [DOI: 10.1109/JOE.2014.2350751]
Kendall M G and Smith B B. 1940. On the method of paired comparisons. Biometrika, 31(3/4): 324-345 [DOI: 10.2307/2332613]
Li C Y, Anwar S and Porikli F. 2020. Underwater scene prior inspired deep underwater image and video enhancement. Pattern Recognition, 98: #107038 [ DOI: 10.1016/j.patcog.2019.107038 http://dx.doi.org/10.1016/j.patcog.2019.107038 ]
Li C Y, Guo C L, Ren W Q, Cong R M, Hou J H, Kwong S and Tao D C. 2019. An underwater image enhancement benchmark dataset and beyond. IEEE Transactions on Image Processing, 99: 4376-4389 [DOI: 10.1109/TIP.2019.2955241]
Li C Y, Guo J C, Cong R M, Pang Y W and Wang B. 2016. Underwater image enhancement by dehazing with minimum information loss and histogram distribution prior. IEEE Transactions on Image Processing, 25(12): 5664-5677 [DOI: 10.1109/TIP.2016.2612882]
Liu L X, Hua Y, Zhao Q J, Huang H and Bovik A C. 2016. Blind image quality assessment by relative gradient statistics and adaboosting neural network. Signal Processing: Image Communication, 40: 1-15 [DOI: 10.1016/j.image.2015.10.005]
Liu L X, Liu B, Huang H and Bovik A C. 2014. No-reference image quality assessment based on spatial and spectral entropies. Signal Processing: Image Communication, 29(8): 856-863 [DOI: 10.1016/j.image.2014.06.006]
Liu R S, Fan X, Zhu M, Hou M J and Luo Z X. 2020. Real-world underwater enhancement: challenges, benchmarks, and solutions under natural light. IEEE Transactions on Circuits and Systems for Video Technology, 30(12): 4861-4875 [DOI: 10.1109/tcsvt.2019.2963772]
McGlamery B L. 1980. A computer model for underwater camera systems//Proceedings of SPIE 0208 Ocean Optics VI. Monterey, Mexico: SPIE: #208 [ DOI: 10.1117/12.958279 http://dx.doi.org/10.1117/12.958279 ]
Mittal A, Moorthy A K and Bovik A C. 2012. No-reference image quality assessment in the spatial domain. IEEE Transactions on Image Processing, 21(12): 4695-4708 [DOI: 10.1109/TIP.2012.2214050]
Moorthy A K and Bovik A C. 2011. Blind image quality assessment: from natural scene statistics to perceptual quality. IEEE Transactions on Image Processing, 20(12): 3350-3364 [DOI: 10.1109/TIP.2011.2147325]
Panetta K, Gao C and Agaian S. 2016. Human-visual-system-inspired underwater image quality measures. IEEE Journal of Oceanic Engineering, 41(3): 541-551 [DOI: 10.1109/JOE.2015.2469915]
Pearson E S and Hartley H O. 1974. Biometrika tables for statisticians. Biometrics, 30(2): #372 [DOI: 10.2307/2529662]
Saad M A, Bovik A C and Charrier C. 2012. Blind image quality assessment: a natural scene statistics approach in the DCT domain. IEEE Transactions on Image Processing, 21(8): 3339-3352 [DOI: 10.1109/TIP.2012.2191563]
Sheinin M and Schechner Y Y. 2016. The next best underwater view//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, USA: IEEE: 3764-3773 [ DOI: 10.1109/CVPR.2016.409 http://dx.doi.org/10.1109/CVPR.2016.409 ]
Song W, Wang Y, Huang D M, Liotta A and Perra C. 2020. Enhancement of underwater images with statistical model of background light and optimization of transmission map. IEEE Transactions on Broadcasting, 66(1): 153-169 [DOI: 10.1109/TBC.2019.2960942]
Xue W F, Mou X Q, Zhang L, Bovik A C and Feng X C. 2014. Blind image quality assessment using joint statistics of gradient magnitude and Laplacian features. IEEE Transactions on Image Processing, 23(11): 4850-4862 [DOI: 10.1109/TIP.2014.2355716]
Yang M, Du Y X, Huang Y, Liu H T, Wei Z Q, Hu J T, Hu K and Sheng Z B. 2019. Preselection based subjective preference evaluation for the quality of underwater images//Proceedings of 2019 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR). Long Beach, USA: IEEE: 34-43
Yang M and Sowmya A. 2015. An underwater color image quality evaluation metric. IEEE Transactions on Image Processing, 24(12): 6062-6071 [DOI: 10.1109/TIP.2015.2491020]
Zhang L, Zhang L and Bovik A C. 2015. A feature-enriched completely blind image quality evaluator. IEEE Transactions on Image Processing, 24(8): 2579-2591 [DOI: 10.1109/TIP.2015.2426416]
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