UNDERWATER IMAGE ENHANCEMENT TECHNIQUE BASED ON CYCLEGAN AND FREQUENCY DECOMPOSITION CORRECTION MODEL
Volume 7, Issue 2, Pp 42-49, 2025
DOI: https://doi.org/10.61784/jcsee3046
Author(s)
LuHeng Wang, HaoLong Qi, LiYe Zhang*
Affiliation(s)
School of Computer Science and Technology, Shandong University of Technology, Zibo 255000, Shandong, China.
Corresponding Author
LiYe Zhang
ABSTRACT
Enhancing underwater images is a challenging task due to degradation from light scattering, absorption, and low contrast, which obscure important details. This paper proposes a novel method that combines Cycle-Consistent Generative Adversarial Network (CycleGAN) with Frequency Decomposition Correction to improve underwater image quality. Initially, CycleGAN was used to generate clearer images from blurred underwater photos, addressing distortions caused by the underwater environment. Next, Frequency Decomposition Correction separates the image into low-frequency (smooth areas) and high-frequency (details and edges) components. The low-frequency component is enhanced for better clarity, while the high-frequency component is sharpened to improve fine details and edges. The enhanced images are evaluated using PSNR, UCIQE, and UIQM metrics. Results show that CycleGAN significantly improves image quality, and the additional Frequency Decomposition Correction boosts clarity, contrast, and detail further. This combined method presents a promising solution for underwater image enhancement, with potential applications in marine research, underwater navigation, and environmental monitoring.
KEYWORDS
CycleGAN; Frequency decomposition; Wavelet transform; CLAHE
CITE THIS PAPER
LuHeng Wang, HaoLong Qi, LiYe Zhang. Underwater image enhancement technique based on CycleGAN and frequency decomposition correction model. Journal of Computer Science and Electrical Engineering. 2025, 7(2): 42-49. DOI: https://doi.org/10.61784/jcsee3046.
REFERENCES
[1] Lepcha D C, Goyal B, Dogra A, et al. A deep journey into image enhancement: A survey of current and emerging trends. Information Fusion, 2023, 93:36-76.
[2] Moghimi M K, Mohanna F. Real-time underwater image enhancement: a systematic review. Journal of Real-Time Image Processing, 2021, 18(5):1509-1525.
[3] Kavitha S T, Vamsidhar A, Kumar S G, et al. Underwater Image Enhancement using Fusion of CLAHE and Total Generalized Variation. Engineering Letters, 2023, 31(4).
[4] Wang Y, Zhang J, Cao Y, et al. A deep CNN method for underwater image enhancement. In: 2017 IEEE International Conference on Image Processing (ICIP). IEEE, 2017:1382-1386.
[5] Chen X, Yu J, Kong S, et al. Towards real-time advancement of underwater visual quality with GAN. IEEE Transactions on Industrial Electronics, 2019, 66(12):9350-9359.
[6] Du R, Li W, Chen S, Li C, Zhang Y. Unpaired Underwater Image Enhancement Based on CycleGAN. Information, 2022, 13:1.
[7] Niu Yuzhen, Zhang Lingxin, Lan Jie, et al. Non-paired underwater image enhancement based on frequency division generative adversarial network. Acta Sinica, 2020, 1-18.
[8] Shah R, Sheikh A, Shukla S, et al. Comparing effectiveness of gan and clahe for enhancing underwater images. In: 2023 7th International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2023:1499-1503.
[9] Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets. Advances in Neural Information Processing Systems, 2014, 27.
[10] Wu J, Liu X, Lu Q, et al. FW-GAN: Underwater image enhancement using generative adversarial network with multi-scale fusion. Signal Processing: Image Communication, 2022, 109:116855.
[11] Zhu J Y, Park T, Isola P, et al. Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision. 2017:2223-2232.
[12] Du R, Li W, Chen S, et al. Unpaired underwater image enhancement based on CycleGAN. Information, 2021, 13(1):1.
[13] Saifullah S, Suryotomo A P, Dre?ewski R, et al. Optimizing brain tumor segmentation through CNN U-Net with CLAHE-HE image enhancement. In: 2023 1st International Conference on Advanced Informatics and Intelligent Information Systems (ICAI3S 2023). Atlantis Press, 2024:90-101.
[14] Kurinjimalar R, Pradeep J, Harikrishnan M. Underwater Image Enhancement Using Gaussian Pyramid, Laplacian Pyramid and Contrast Limited Adaptive Histogram Equalization. In: 2024 IEEE 3rd World Conference on Applied Intelligence and Computing (AIC). IEEE, 2024:729-734.
[15] Tang X, Wu Y. Single Underwater Image Enhancement Based on Transmission Map Weighted Fusion and Adaptive Color Correction. In: 2023 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML). IEEE, 2023:25-28.