Residual SuperPoint underwater coral reef image registration method based on adaptive equalization sample

Sun Rui; Ma Yi; Zhang Feifei; Hu Yabin; Cui Xuerong

doi:10.12284/hyxb2025128

Volume 47 Issue 11

Nov. 2025

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Sun Rui，Ma Yi，Zhang Feifei, et al. Residual SuperPoint underwater coral reef image registration method based on adaptive equalization sample[J]. Haiyang Xuebao，2025, 47(11)：154–166 doi: 10.12284/hyxb2025128

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Residual SuperPoint underwater coral reef image registration method based on adaptive equalization sample

doi: 10.12284/hyxb2025128

Sun Rui^{1, 2
,},
Ma Yi^{1
,
,},
Zhang Feifei^{1, 2},
Hu Yabin¹,
Cui Xuerong²

1.
First Institute of Oceanology, Ministry of Natural Resources, Qingdao 266061, China
2.
College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China

Received Date: 2025-06-11
Rev Recd Date: 2025-09-15

Available Online: 2025-09-29

Publish Date: 2025-11-30

Abstract

Abstract

Coral reef ecosystems are the most biologically diverse marine ecosystems on Earth and form the basis for coral reef research and conservation. Underwater monitoring is an important method for obtaining coral reef data. For underwater environments characterized by spectral complexity and high structural complexity, this paper proposes a residual SuperPoint underwater coral reef image registration method based on adaptive equalization sample. To address the issue of Visual Geometry Group (VGG) networks causing partial loss of high-frequency features and low feature extraction efficiency, a residual module is introduced into the encoding network to retain original features while reducing fitting difficulty and improving the accuracy of image feature point extraction. To address the issue of feature point extraction easily neglecting negative samples, we propose an adaptive equalization sample comparison loss function that incorporates a difficult negative sample mining mechanism. This improves parameter optimization efficiency, accelerates convergence speed, and enhances feature point extraction accuracy. Experiments conducted using the Hainan Jiajing Island underwater coral reef optical dataset, COCO, and HPatches datasets demonstrate that on the HPatches dataset, the residual SuperPoint algorithm achieves a feature point overlap rate of 61.7%, outperforming comparison algorithms by 4.8% to 23.1%. In underwater coral reef scenarios, Residual SuperPoint achieved a 11.8% improvement in structural similarity index measure (SSIM) and a 22.60% increase in mutual information (MI) compared to the classic SuperPoint at the image-level registration evaluation metrics, while maintaining comparable root mean square error (RMSE). Compared to other traditional algorithms, it demonstrated optimal performance in both structural similarity index and mutual information metrics, with suboptimal RMSE. The proposed method provides technical support for coral reef surveys, ecological monitoring, and related fields.
- underwater image registration,
- coral reefs,
- SuperPoint,
- image enhancement

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References(32)

References

[1]	龙丽娟, 杨芳芳, 韦章良. 珊瑚礁生态系统修复研究进展[J]. 热带海洋学报, 2019, 38(6): 1−8. Long Lijuan, Yang Fangfang, Wei Zhangliang. A review on ecological restoration techniques of coral reefs[J]. Journal of Tropical Oceanography, 2019, 38(6): 1−8.
[2]	Ai Bo, Liu Xue, Wen Zhen, et al. A novel coral reef classification method combining radiative transfer model with deep learning[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2024, 17: 13400−13412. doi: 10.1109/JSTARS.2024.3430899
[3]	Hedley J D, Roelfsema C M, Chollett I, et al. Remote sensing of coral reefs for monitoring and management: a review[J]. Remote Sensing, 2016, 8(2): 118. doi: 10.3390/rs8020118
[4]	张飞飞, 任广波, 胡亚斌, 等. 融合地理空间认知的珊瑚礁地貌单元高分遥感分类方法[J]. 海洋技术学报, 2023, 42(1): 1−15. Zhang Feifei, Ren Guangbo, Hu Yabin, et al. A high-resolution remote sensing classification method of coral reef geomorphic units integrating geospatial cognition[J]. Journal of Ocean Technology, 2023, 42(1): 1−15.
[5]	Teague J, Megson-Smith D A, Allen M J, et al. A review of current and new optical techniques for coral monitoring[J]. Oceans, 2022, 3(1): 30−45. doi: 10.3390/oceans3010003
[6]	郑金辉, 任广波, 胡亚斌, 等. 生物天敌暴发导致珊瑚礁退化的高分遥感监测与分析—以南海太平岛为例[J]. 热带地理, 2023, 43(10): 1856−1873. Zheng Jinhui, Ren Guangbo, Hu Yabin, et al. High resolution remote sensing monitoring and analysis of coral reef degradation caused by outbreaks of biological natural enemies: a case study of the Taiping Island in the South China Sea[J]. Tropical Geography, 2023, 43(10): 1856−1873.
[7]	Turner J A, Polunin N V C, Field S N, et al. Measuring coral size-frequency distribution using stereo video technology, a comparison with in situ measurements[J]. Environmental Monitoring and Assessment, 2015, 187(5): 234. doi: 10.1007/s10661-015-4431-8
[8]	Mahmood A, Bennamoun M, An Senjian, et al. Deep image representations for coral image classification[J]. IEEE Journal of Oceanic Engineering, 2019, 44(1): 121−131. doi: 10.1109/JOE.2017.2786878
[9]	Ghaffar A A, Choi G S. A two-stream deep learning framework for robust coral reef health classification: insights and interpretability[J]. IEEE Access, 2025, 13: 78490−78512. doi: 10.1109/ACCESS.2025.3561226
[10]	Zheng Ziqiang, Liang Haixin, Wut F H, et al. HKCoral: benchmark for dense coral growth form segmentation in the wild[J]. IEEE Journal of Oceanic Engineering, 2025, 50(2): 697−713. doi: 10.1109/JOE.2024.3494112
[11]	Casoli E, Ventura D, Mancini G, et al. High spatial resolution photo mosaicking for the monitoring of coralligenous reefs[J]. Coral Reefs, 2021, 40(4): 1267−1280. doi: 10.1007/s00338-021-02136-4
[12]	Lowe D G. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 2004, 60(2): 91−110. doi: 10.1023/B:VISI.0000029664.99615.94
[13]	Bay H, Tuytelaars T, Van Gool L. Surf: speeded up robust features[C]//Proceedings of the 9th European Conference on Computer Vision. Graz: Springer, 2006: 404−417.
[14]	Rublee E, Rabaud V, Konolige K, et al. ORB: an efficient alternative to SIFT or SURF[C]//Proceedings of the 2011 International Conference on Computer Vision. Barcelona: IEEE, 2011: 2564−2571.
[15]	Pang Siqi, Ge Junyao, Hu Lei, et al. RTV-SIFT: harnessing structure information for robust optical and SAR image registration[J]. Remote Sensing, 2023, 15(18): 4476. doi: 10.3390/rs15184476
[16]	DeTone D, Malisiewicz T, Rabinovich A. SuperPoint: self-supervised interest point detection and description[C]//Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Salt Lake City: IEEE, 2018: 337−33712.
[17]	Zou Bin, Li Haolin, Zhang Lamei. Self-supervised SAR image registration with SAR-superpoint and transformation aggregation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5201115.
[18]	曾旭东, 樊绍胜, 续尚植, 等. 低光照环境下基于轻量级SuperPoint的单目VI-SLAM算法[J]. 激光与光电子学进展, 2024, 61(18): 1815001. Zeng Xudong, Fan Shaosheng, Xu Shangzhi, et al. Monocular VI-SLAM algorithm based on lightweight SuperPoint network in low-light environment[J]. Laser & Optoelectronics Progress, 2024, 61(18): 1815001.
[19]	Li Zhaoyang, Cao Jie, Hao Qun, et al. DAN-SuperPoint: self-supervised feature point detection algorithm with dual attention network[J]. Sensors, 2022, 22(5): 1940. doi: 10.3390/s22051940
[20]	赵悦, 储开斌, 张继, 等. 面向复杂环境的特征匹配算法[J]. 计算机应用与软件, 2025, 42(1): 264−270,293. Zhao Yue, Chu Kaibin, Zhang Ji, et al. Feature point matching algorithms for complex environments[J]. Computer Applications and Software, 2025, 42(1): 264−270,293.
[21]	Zhong Jiageng, Li Ming, Zhang Hanqi, et al. Fine-grained 3D modeling and semantic mapping of coral reefs using photogrammetric computer vision and machine learning[J]. Sensors, 2023, 23(15): 6753. doi: 10.3390/s23156753
[22]	Zhong J, Li M, Gruen A, et al. Cutting-edge 3D reconstruction solutions for underwater coral reef images: a review and comparison[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2025, 230: 779−803.
[23]	Xu Z Q J, Zhang Yaoyu, Xiao Yanyang. Training behavior of deep neural network in frequency domain[C]//Proceedings of the 26th International Conference on Neural Information Processing. Sydney: Springer, 2019: 264−274.
[24]	Francazi E, Baity-Jesi M, Lucchi A. A theoretical analysis of the learning dynamics under class imbalance[C]//Proceedings of the 40th International Conference on Machine Learning. Honolulu: JMLR. org, 2023: 413.
[25]	Gao X, Xie D, Zhang Y, et al. A comprehensive survey on imbalanced data learning[J]. arXiv preprint arXiv: 2502.08960, 2025.
[26]	Wu Xin, Zhang Lin, Huang Jipeng, et al. Underwater image enhancement via modeling white degradation[J]. IEEE Journal of Oceanic Engineering, 2024, 49(4): 1220−1232. doi: 10.1109/JOE.2024.3429653
[27]	Sang V Q, 冯鹏, 汤斌, 等. 基于米氏散射理论的水中悬浮颗粒物散射特性计算[J]. 激光与光电子学进展, 2015, 52(1): 013001. Sang V Q, Feng Peng, Tang Bin, et al. Study on properties of light scattering based on Mie scattering theory for suspended particles in water[J]. Laser & Optoelectronics Progress, 2015, 52(1): 013001.
[28]	Wang Yudong, Guo Jichang, Gao Huan, et al. UIEC^2-Net: CNN-based underwater image enhancement using two color space[J]. Signal Processing: Image Communication, 2021, 96: 116250. doi: 10.1016/j.image.2021.116250
[29]	Zhang Hanqi, Li Ming, Pan Xiaotian, et al. Novel approaches to enhance coral reefs monitoring with underwater image segmentation[C]//Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Wuhan: ISPRS, 2022: 271−277.
[30]	Huo Chunling, Zhang Da, Yang Huanyu. An underwater image denoising method based on high-frequency abrupt signal separation and hybrid attention mechanism[J]. Sensors, 2024, 24(14): 4578. doi: 10.3390/s24144578
[31]	Sarlin P E, DeTone D, Malisiewicz T, et al. Superglue: Learning feature matching with graph neural networks[C]. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020: 4938−4947.
[32]	Srivatsan R A, Zevallos N, Vagdargi P, et al. Registration with a small number of sparse measurements[J]. The International Journal of Robotics Research, 2019, 38(12/13): 1403−1419.