Adaptive Color Correction and Detail-Preserving Fusion for Enhanced Underwater Image Quality and Segmentation

Sudhanshu Maurya; K. Bala Chowdappa; Shanmuga Priya R.; Yogesh H. Bhosale; Gangu Rama Naidu; Anil D.; Kundan Kumar; S. Kanakaprabha

doi:10.48084/etasr.11438

Authors

Sudhanshu Maurya Department of Computer Science & Engineering, Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune, India
K. Bala Chowdappa Department of CSE, G Pulla Reddy Engineering College (Autonomous), Kurnool, Andhrapradesh, India
Shanmuga Priya R. Department of Information Science and Engineering, Ramaiah Institute of Technology, Bengaluru, India
Yogesh H. Bhosale Computer Science & Engineering Department, CSMSS Chh. Shahu College of Engineering, Chhatrapati Sambhajinagar (Aurangabad), Maharashtra, India
Gangu Rama Naidu Department of Electronics and Communication Engineering, Aditya University, Surampalem, India
Anil D. Department of Computer Science and Business System, Dayananda Sagar College of Engineering, Bengaluru, India
Kundan Kumar Department of Electronics and Communication, Government Engineering College Banka, Bihar, India
S. Kanakaprabha Department of AIML, Malla Reddy College of Engineering, Secunderabad, Telangana, India

Volume: 15 | Issue: 4 | Pages: 26011-26018 | August 2025 | https://doi.org/10.48084/etasr.11438

Received: 12 April 2025 | Revised: 3 May 2025 and 19 May 2025 | Accepted: 24 May 2025 | Online: 17 July 2025

Corresponding author: S. Kanakaprabha

Abstract

Underwater images frequently experience significant color distortion, diminished contrast, and decreased visibility. These issues arise due to the selective absorption and scattering of light in aquatic environments. This study introduces an innovative method for enhancing the quality of underwater images through the implementation of adaptive color correction and detail-preserving fusion techniques. The approach addresses the attenuation of red and blue color channels, followed by an adaptive fusion process that enhances contrast and texture while maintaining intricate image details. The findings reveal notable advancements compared to current state-of-the-art techniques, as assessed by widely recognized metrics including the Perceptual Color Quality Index (PCQI), the Underwater Image Quality Measure (UIQM), and the Underwater Color Image Quality Evaluation (UCIQE). Specifically, the method achieves a PCQI score of 1.00 on test shipwreck images, demonstrating a significant improvement in color quality. The enhanced images exhibit more accurate color representation, improved sharpness, and higher contrast, making them suitable for both human observation and further computer vision applications. Additionally, the segmentation performance, as measured by the Geodesic Active Contours (GAC++) algorithm, is markedly improved on the enhanced images, demonstrating the practical utility of the proposed approach for underwater image analysis and object detection. This work sets a new standard for underwater image enhancement, facilitating its application in fields such as marine biology, underwater archaeology, and robotic vision systems.

Keywords:

image processing, PCQI, UIQM, UCIQE, image enhancement, GAC

References

S. Liu, H. Fan, S. Lin, Q. Wang, N. Ding, and Y. Tang, "Adaptive Learning Attention Network for Underwater Image Enhancement," IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 5326–5333, Apr. 2022. DOI: https://doi.org/10.1109/LRA.2022.3156176

Md. H. Mondol, S. P. Biswas, Md. A. Rahman, Md. R. Islam, A. M. Mahfuz-Ur-Rahman, and K. M. Muttaqi, "A New Hybrid Multilevel Inverter Topology With Level Shifted Multicarrier PWM Technique for Harvesting Renewable Energy," IEEE Transactions on Industry Applications, vol. 58, no. 2, pp. 2574–2585, Mar. 2022. DOI: https://doi.org/10.1109/TIA.2022.3146223

J. R. Y. Ramírez, R. R. R. Parraga, and J. A. V. Arcos, "Use of Geomatic Techniques for Mapping Suspended Solids in Aquatic Ecosystems: The Case Study of Guayas River, Ecuador," Engineering, Technology & Applied Science Research, vol. 14, no. 6, pp. 17650–17656, Dec. 2024. DOI: https://doi.org/10.48084/etasr.8664

Z. Jiang, Z. Li, S. Yang, X. Fan, and R. Liu, "Target Oriented Perceptual Adversarial Fusion Network for Underwater Image Enhancement," IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 10, pp. 6584–6598, Oct. 2022. DOI: https://doi.org/10.1109/TCSVT.2022.3174817

F. Liu, Z. Huang, T. Xie, R. Hu, and B. Qi, "Enhancing Underwater Image Quality Assessment with Influential Perceptual Features," Electronics, vol. 12, no. 23, Dec. 2023, Art. no. 4760. DOI: https://doi.org/10.3390/electronics12234760

S. Anwar and C. Li, "Diving deeper into underwater image enhancement: A survey," Signal Processing: Image Communication, vol. 89, Nov. 2020, Art. no. 115978. DOI: https://doi.org/10.1016/j.image.2020.115978

C. Wang, Z. Wu, Z. Han, J. Wang, H. Hu, and X. Li, "Rethinking of Underwater Image Restoration Based on Circularly Polarized Light," Photonics, vol. 11, no. 8, Aug. 2024, Art. no. 773. DOI: https://doi.org/10.3390/photonics11080773

Y. Li, D. Li, Z. Gao, S. Wang, Q. Jiao, and L. bian, "Underwater image enhancement utilizing adaptive color correction and model conversion for dehazing," Optics & Laser Technology, vol. 169, Feb. 2024, Art. no. 110039. DOI: https://doi.org/10.1016/j.optlastec.2023.110039

B. Xiao, X. Gao, and H. Huang, "Optimizing Underwater Image Restoration and Depth Estimation with Light Field Images," Journal of Marine Science and Engineering, vol. 12, no. 6, Jun. 2024, Art. no. 935. DOI: https://doi.org/10.3390/jmse12060935

T. Zhang, Y. Li, and S. Takahashi, "Underwater image enhancement using improved generative adversarial network," Concurrency and Computation: Practice and Experience, vol. 33, no. 22, Jun. 2021, Art. no. e5841. DOI: https://doi.org/10.1002/cpe.5841

D. Dileep, S. Pandi, and Srinivasan, "Deep Learning Based Underwater Image Enhancement Using Hybrid CNN GAN Approach with Self Attention Mechanism," in 2025 8th International Conference on Trends in Electronics and Informatics, Tirunelveli, India, 2025, pp. 987–996. DOI: https://doi.org/10.1109/ICOEI65986.2025.11013142

J. M. Montero and J.-L. Lisani, "Enhancing Underwater Images Using Deep Learning with Subjective Image Quality Integration." arXiv, Jul. 07, 2025.

K.-J. Hu, Y.-T. Pan, L.-W. Jiang, S.-D. Lee, and S.-L. Kao, "A robust underwater image enhancement algorithm," The Journal of Supercomputing, vol. 81, no. 1, Dec. 2024, Art. no. 244. DOI: https://doi.org/10.1007/s11227-024-06719-0

Y. Han, J. Kim, J. Lee, J.-H. Nah, Y.-S. Ho, and W.-C. Park, "Efficient Haze Removal from a Single Image Using a DCP-Based Lightweight U-Net Neural Network Model," Sensors, vol. 24, no. 12, Jun. 2024, Art. no. 3746. DOI: https://doi.org/10.3390/s24123746

S.-C. Pei and C.-Y. Chen, "Underwater Images Enhancement by Revised Underwater Images Formation Model," IEEE Access, vol. 10, pp. 108817–108831, 2022. DOI: https://doi.org/10.1109/ACCESS.2022.3213340

A. Chakrabarti, "Color Constancy by Learning to Predict Chromaticity from Luminance." arXiv, Dec. 07, 2015.

T. Gong, M. Zhang, Y. Zhou, and H. Bai, "Underwater Image Enhancement Based on Color Feature Fusion," Electronics, vol. 12, no. 24, Dec. 2023, Art. no. 4999. DOI: https://doi.org/10.3390/electronics12244999

T. M. Sachin and G. P. Prerana, "Underwater Image Enhancement Using Color Correction and Fusion," in VLSI, Communication and Signal Processing" Select Proceedings of the 5th International Conference, Online, 2023, pp. 97–105. DOI: https://doi.org/10.1007/978-981-99-0973-5_7

Q. Jiang, Y. Zhang, F. Bao, X. Zhao, C. Zhang, and P. Liu, "Two-step domain adaptation for underwater image enhancement," Pattern Recognition, vol. 122, Feb. 2022, Art. no. 108324. DOI: https://doi.org/10.1016/j.patcog.2021.108324

G. Hou, J. Li, G. Wang, Z. Pan, and X. Zhao, "Underwater image dehazing and denoising via curvature variation regularization," Multimedia Tools Appl., vol. 79, no. 27–28, pp. 20199–20219, Jul. 2020. DOI: https://doi.org/10.1007/s11042-020-08759-z

G. Hou, X. Zhao, Z. Pan, H. Yang, L. Tan, and J. Li, "SUID: Synthetic Underwater Image Dataset." IEEE DataPort, Jun. 29, 2020.

L. Ke et al., "Haze removal from a single remote sensing image based on a fully convolutional neural network," Journal of Applied Remote Sensing, vol. 13, no. 3, Aug. 2019, Art. no. 036505. DOI: https://doi.org/10.1117/1.JRS.13.036505

D. S. da Silva, C. S. Nascimento, S. K. Jagatheesaperumal, and V. H. C. de Albuquerque, "Mammogram Image Enhancement Techniques for Online Breast Cancer Detection and Diagnosis," Sensors, vol. 22, no. 22, Nov. 2022, Art. no. 8818. DOI: https://doi.org/10.3390/s22228818

F. Gao, K. Wang, Z. Yang, Y. Wang, and Q. Zhang, "Underwater Image Enhancement Based on Local Contrast Correction and Multi-Scale Fusion," Journal of Marine Science and Engineering, vol. 9, no. 2, Feb. 2021, Art. no. 225. DOI: https://doi.org/10.3390/jmse9020225

K. Liao and X. Peng, "Underwater image enhancement using multi-task fusion," Plos One, vol. 19, no. 2, Feb. 2024, Art. no. e0299110. DOI: https://doi.org/10.1371/journal.pone.0299110

D. Zhang et al., "Underwater image enhancement via multi-scale fusion and adaptive color-gamma correction in low-light conditions," Engineering Applications of Artificial Intelligence, vol. 126, no. C, Nov. 2023, Art. no. 106972. DOI: https://doi.org/10.1016/j.engappai.2023.106972

C.-Y. Li, J.-C. Guo, R.-M. Cong, Y.-W. Pang, and B. Wang, "Underwater Image Enhancement by Dehazing With Minimum Information Loss and Histogram Distribution Prior," IEEE Transactions on Image Processing, vol. 25, no. 12, pp. 5664–5677, Dec. 2016. DOI: https://doi.org/10.1109/TIP.2016.2612882

H. Zhang, L. Gong, X. Li, F. Liu, and J. Yin, "An underwater imaging method of enhancement via multi-scale weighted fusion," Frontiers in Marine Science, vol. 10, May 2023, Art. no. 1150593. DOI: https://doi.org/10.3389/fmars.2023.1150593

C. Li et al., "An Underwater Image Enhancement Benchmark Dataset and Beyond." arXiv, Nov. 26, 2019.

"The EUVP dataset: Enhanced Underwater Visual Perception Dataset." Minnesota Interactive Robotics and Vision Laboratory. [Online]. Available: https://irvlab.cs.umn.edu/resources/euvp-dataset.

D. Berman, D. Levy, S. Avidan, and T. Treibitz, "Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 8, pp. 2822–2837, Aug. 2021.

G. Hou, J. Li, G. Wang, H. Yang, B. Huang, and Z. Pan, "A novel dark channel prior guided variational framework for underwater image restoration," Journal of Visual Communication and Image Representation, vol. 66, Jan. 2020, Art. no. 102732. DOI: https://doi.org/10.1016/j.jvcir.2019.102732

Z. Liang, Y. Wang, X. Ding, Z. Mi, and X. Fu, "Single underwater image enhancement by attenuation map guided color correction and detail preserved dehazing," Neurocomputing, vol. 425, pp. 160–172, Feb. 2021. DOI: https://doi.org/10.1016/j.neucom.2020.03.091

H. Lu, Y. Li, L. Zhang, and S. Serikawa, "Contrast enhancement for images in turbid water," Journal of the Optical Society of America A, vol. 32, no. 5, pp. 886–893, May 2015. DOI: https://doi.org/10.1364/JOSAA.32.000886