Adaptive illumination enhancement and feature enhancement algorithm based on unmanned surface vehicle perception

ZOU Cunlong , LV Zhengkai , WANG Ning

doi:10.16411/j.cnki.issn1006-7736.2025.02.011

Journal of Dalian Maritime University >

2025 , Vol. 51 >Issue 2: 97 - 105

DOI: https://doi.org/10.16411/j.cnki.issn1006-7736.2025.02.011

Adaptive illumination enhancement and feature enhancement algorithm based on unmanned surface vehicle perception

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（College of Marine Engineering, Dalian Maritime University, Dalian 116026,China）

Online published: 2025-02-09

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Abstract

This paper proposes an adaptive correction and feature enhancement image preprocessing algorithm to address the problem of feature point matching failure caused by uneven lighting and blurry perceived image texture during autonomous navigation of unmanned ships. Firstly, Unsharp Masking was used to enhance image details. Then, multi-scale Gaussian convolution was used to extract lighting components in the brightness channel, and two-dimensional gamma correction was used to homogenize brightness. Gaussian filtering was applied to the tone channel to suppress low-pass noise. Finally, histogram homogenization algorithm was used to improve image contrast. Compared with the MSR algorithm, MSRCR algorithm, and SSR algorithm, the average gradient of this algorithm has increased by up to 50.55%, 151.21%, and 43.68% respectively, and the feature matching accuracy has increased by up to 86.81%, 176.08%, and 61.96% respectively. This research provides preprocessing techniques for visual perception images in autonomous navigation of unmanned ships, improving image quality and having certain application value.

Key words： unmanned surface vehicle; uneven illumination; two-dimensional gamma function; multi dimensional gaussian function; feature point matching

Cite this article

ZOU Cunlong , LV Zhengkai , WANG Ning . Adaptive illumination enhancement and feature enhancement algorithm based on unmanned surface vehicle perception[J]. Journal of Dalian Maritime University, 2025 , 51(2) : 97 -105 . DOI: 10.16411/j.cnki.issn1006-7736.2025.02.011

References

[1]WANG N, ZHANG Y, AHN C K, et al. Autonomous pilot of unmanned surface vehicles: Bridging path planning and tracking[J]. IEEE Transactions on Vehicular Technology, 2021, 71(3): 2358-2374.
[2]何红坤, 王宁. 欠驱动无人船单目视觉伺服镇定控制[J]. 中国舰船研究, 2022, 17(5): 166-174, 183.
HE H K, WANG N. Underactuated unmanned ship monocular visual servo stabilization control[J]. Chinese Journal of Ship Research, 2022, 17(5): 166-174, 183. (in Chinese)
[3]WANG N, HE H. K Dynamics-level finite-time fuzzy monocular visual servo of an unmanned surface vehicle[J]. IEEE Transactions on Industrial Electronics, 2019, 67(11): 9648-9658.
[4]郭强, 王嘉琪, 张显库, 等. 基于 BLF 的浅水效应下无人船自适应路径跟踪控制[J]. 中国舰船研究, 2024.
GUO Q, WANG J Q, ZHANG X K, et al. Adaptive Path Tracking Control of Unmanned Ships under Shallow Water Effect Based on BLF [J]. Chinese Journal of Ship Research, 2024. (in Chinese)
[5]WANG N, HE H K, HOU Y, et al. Model-free visual servo swarming of manned-unmanned surface vehicles with visibility maintenance and collision avoidance[J]. IEEE Transactions on Intelligent Transportation Systems, 2023.
[6]LAND E H, MCCANN J J. Lightness and retinex theory[J]. Josa, 1971, 61(1): 1-11.
[7]GOPAL M, YEDULAPURAM S, KOMMABATLA M. Single scale RETINEX algorithm for low light image enhancement[C]//AIP Conference Proceedings. AIP Publishing, 2024, 2971(1).
[8]WANG X X, YI R, SUN M Y, et al. Low-light Image Enhancement Algorithm Based on Improved Multi-scale Retinex with Adaptive Brightness Compensation[C]//2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, 2024: 2234-2239.
[9]PARTHASARATHY S, SANKARAN P. An automated multi scale retinex with color restoration for image enhancement[C]//2012 National Conference on Communications (NCC). IEEE, 2012: 1-5.
[10]DHAL K G, DAS A, RAY S, et al. Histogram equalization variants as optimization problems: a review[J]. Archives of Computational Methods in Engineering, 2021, 28: 1471-1496. Zuiderveld K. Contrast limited adaptive histogram equalization[M]Graphics gems IV. 1994: 474-485.
[11]HU C S, LI H, MA T, et al. An improved image enhancement algorithm: Radial contrast-limited adaptive histogram equalization[J]. Multimedia Tools and Applications, 2024: 1-13.
[12]CHANG Y K, JUNG C K, KE P, et al. Automatic contrast-limited adaptive histogram equalization with dual gamma correction[J]. IEEE Access, 2018, 6: 11782-11792.
[13]WEI C, WANG W J, YANG W H, et al. Deep retinex decomposition for low-light enhancement[J]. arXiv preprint arXiv, 2018,1808:04560.
[14]HAI J, XUAN Z, YANG R, et al. R2rnet: Low-light image enhancement via real-low to real-normal network[J]. Journal of Visual Communication and Image Representation, 2023, 90: 103712.
[15]张航瑛,王雪琦,王华英,等.基于明度分量的Retinex-Net图像增强改进方法[J].物理学报,2022,71(11):107-115.
ZHANG H Y,WANG X Q,WANG H Y, et al. Improved Retinex Net image enhancement method based on brightness component[J]. Acta Physica Sinica, 2022,71(11):107-115. (in Chinese)
[16]张金泽, 赵红, 王宁, 等. 密集障碍物下无人艇模糊双窗口 DWA 避障算法[J]. 中国舰船研究, 2021, 16(6): 10-18.
ZHANG J Z, ZHAO H, WANG N, et al. Fuzzy dual window DWA obstacle avoidance algorithm for unmanned boats under dense obstacles[J]. Chinese Journal of Ship Research, 2021, 16(6): 10-18. (in Chinese)
[17] WANG N, GAO Y, ZHANG X. Data-driven performance-prescribed reinforcement learning control of an unmanned surface vehicle[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021, 32(12): 5456-5467.
[18] KONG X Y, LIU L, QIAN Y S. Low-light image enhancement via poisson noise aware retinex model[J]. IEEE Signal Processing Letters, 2021, 28: 1540-1544.
[19]刘志成,王殿伟,刘颖,等.基于二维伽马函数的光照不均匀图像自适应校正算法[J].北京理工大学学报,2016,36(2):191-196+214.
LIU Z C, WANG D W, LIU Y, et al. Adaptive correction algorithm for uneven illumination images based on two-dimensional gamma function[J]. Journal of Beijing Institute of Technology,2016,36(2):191-196+214.(in Chinese)
[20] LEE S, KWON H, HAN H, et al. A space-variant luminance map based color image enhancement[J]. IEEE Transactions on Consumer Electronics, 2010, 56(4): 2636-2643.
[21]BOVCON, BORJA, JON MUHOVIČ, et al. "Stereo obstacle detection for unmanned surface vehicles by IMU-assisted semantic segmentation."Robotics and Autonomous Systems , 2018,104: 1-13.
[22]赵瑞祥,畅海峰,白晓勇,等.面向无人船感知的低对比度水面图像增强方法[J].舰船科学技术,2023,45(14):83-89.
ZHAO R X,CHANG H F,BAI X Y, et al. Low contrast water surface image enhancement method for unmanned ship perception[J]. Ship Science and Technology,2023,45(14):83-89.
[23]胡瀚文,王猛,程卫平,等.水下视觉SLAM的图像滤波除尘与特征增强算法[J].机器人,2023,45(2):197-206.
HU H W, WANG M, CHENG W P, et al. Image filtering, dust removal, and feature enhancement algorithm for underwater visual SLAM[J]. Robot,2023,45(2):197-206.
[24]黄静,欧余韬.光照不均匀条件下无人机航拍低照度图像增强方法[J].现代电子技术,2025,48(1):55-59.
HUANG J, OU Y T. Enhancement method for low light images captured by unmanned aerial vehicles under uneven lighting conditions[J]. Modern Electronic Technology,2025,48(1):55-59.
[25]LUO C, YANG W, HUANG P, et al. Overview of image matching based on ORB algorithm[C]//Journal of Physics: Conference Series. IOP Publishing, 2019, 1237(3): 032020.
[26]JAKUBOVIĆ A, VELAGIĆ J. Image feature matching and object detection using brute-force matchers[C]//2018 International Symposium ELMAR. IEEE, 2018: 83-86.

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