Enhancement of Low-Light Images using Zero-DiDCE+ and Bilateral Filtering
DOI:
https://doi.org/10.32628/IJSRST26133104Keywords:
Low-Light Image Enhancement, Zero-DiDCE+, Bilateral Filter, Image Processing, Noise Reduction, Contrast EnhancementAbstract
Enhancing images taken in low light is an important part of image processing because pictures taken in low light often have low visibility, noise, and less contrast. This paper presents a technique for improving low-light images by integrating the Zero-DiDCE+ model with a Bilateral Filter. The Zero-DiDCE+ method makes things brighter and more contrast without needing paired training data. The Bilateral Filter, on the other hand, helps get rid of noise and keep edges. The suggested method makes images look better, keeps their natural look, and makes them clearer. The results show that this method works well for things like computer vision systems and surveillance.
Downloads
References
Jiang, Y., Gong, X., Liu, D., Cheng, Y., Fang, Mi, A., Luo, W., Qiao, Y., & Huo, Z. (2024). Rethinking Zero-DCE for Low-Light Image Enhancement. Neural Processing Letters, 56:93.
C., Shen, X., & Wang, Z. (2021). EnlightenGAN: Deep Light Enhancement Without Paired Supervision. IEEE Transactions on Image Processing.
Li, Q., Li, Z., & Wang, H. (2023). Low-Light Image Enhancement based on Zero-DCE and Structural Similarity Loss. ICIAAI 2023.Gao, R., Dong, J., Li, Q., & Feng, L. (2023).
Improved Zero-DCE with Denoise-Net for Noisy Low-Light Images. Journal of Computational Methods, 2023.
Dai, H., Zhang, B., Zhao, H., et al. (2024). Enhancing Low-Light Remote Sensing Images using Zero-DCE++. Proceedings of SPIE, 2024.
C. Guo et al., “Zero-reference deep curve estimation for low-light image enhancement,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2020, pp. 1777–1786.
K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, “Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising,” IEEE Trans. Image Process., vol. 26, no. 7, pp. 3142–3155, Jul. 2017.
S. M. Pizer et al., “Adaptive histogram equalization and its variations,” Comput. Vis., Graph., Image Process., vol. 39, no. 3, pp. 355–368, Sep. 1987.
10. Y.-T. Kim, “Contrast enhancement using brightness preserving bihistogram equalization,” IEEE Trans. Consum. Electron., vol. 43, no. 1, pp. 1–8, Feb. 1997.
Rudra Kalyan, Dr. RAVI KUMAR JOTOTH, RISHENDRA TIRUVEEDHULA, “A NEW CONCATENATED METHOD FOR DEEP CURVE ESTIMATION USING LOW WEIGHT CNN FOR LOW LIGHT IMAGE ENHANCEMENT”, 2023 2nd International Conference for Innovation in Technology (INOCON) Bangalore, India. Mar 3-5, 2023.
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: From error visibility to structural similarity,” IEEE Trans. Image Process., vol. 13, no. 4, pp. 600–612, Apr. 2004.
Q. Jiang, Y. Mao, R. Cong, W. Ren, C. Huang and F. Shao, "Unsupervised Decomposition and CorrectionNetwork for Low-Light Image Enhancement," in IEEE Transactions on Intelligent Transportation Systems, vol.23, no. 10, pp. 19440-19455, Oct. 2022, doi: 10.1109/TITS.2022.3165176.
Yu, N., Li, J., & Hua, Z. (2023). Fla-net: multi-stage modular network for low-light image enhancement. The Visual Computer, 39(4), 1251-1270.
Maloth, V., & Jatoth, R. K. (2024). Non-Reference Enhanced Low-Light Image Estimation Using Zero-DCE with Filtering Refinement. Research Square.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 International Journal of Scientific Research in Science and Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
https://creativecommons.org/licenses/by/4.0
