A Study of Image Denoising Methods through U-net Based Machine Learning Design
DOI:
https://doi.org/10.58445/rars.3177Abstract
There have been significant advances in the field of image denoising using machine learning techniques. In state-of-the-art methods, system complexity and system performance have been steadily improving over the years. Still, there is additional work to simplify the networks while keeping high performance. In this paper, we introduce several image denoising techniques, including those state-of-the-art methods with system complexity and the simplified ones. We did experiments using NAFNet, a deep learning denoising model that made the simplification through avoiding nonlinear activation functions. We found that by refining the dataset and introducing new training images, the quality of the results could be substantially improved. Furthermore, we experimented with different model structures and found that we can reduce model complexity substantially, while model effectiveness does not diminish much.
References
L. Fan, F. Zhang, H. Fan, and C. Zhang, “Brief review of image denoising techniques,” Visual Computing for Industry, Biomedicine, and Art, vol. 2, no. 1, Jul. 2019, doi: 10.1186/s42492-019-0016-7.
N. Wani and K. Raza, “Multiple kernel-learning approach for medical image analysis,” in Soft Computing Based Medical Image Analysis, pp. 31–47, 2018, doi: 10.1016/b978-0-12-813087-2.00002-6.
R. Verma and J. Ali, “A comparative study of various types of image noise and efficient noise removal techniques,” Int. J. Adv. Res. Comput. Sci. Softw. Eng., vol. 3, pp. 617–622, 2013.
R. C. Gonzalez and R. E. Woods, Digital Image Processing, 3rd ed. Upper Saddle River, NJ, USA: Prentice-Hall, 2006.
Z. Al-Ameen, S. Al Ameen, and G. Sulong, “Latest methods of image enhancement and restoration for computed tomography: a concise review,” Appl. Med. Inf., vol. 36, no. 1, pp. 1–12, 2015.
J. H. Hou, “Research on image denoising approach based on wavelet and its statistical characteristics,” Ph.D. dissertation, Huazhong Univ. Sci. Technol., Wuhan, China, 2007.
A. Buades, B. Coll, and J. M. Morel, “A non-local algorithm for image denoising,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., San Diego, CA, USA, 2005, pp. 60–65, doi: 10.1109/CVPR.2005.38.
Y. Tai, J. Yang, X. Liu, and C. Xu, “MemNet: A persistent memory network for image restoration,” in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), Oct. 2017, pp. 4549–4557, doi: 10.1109/ICCV.2017.486.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Proc. Int. Conf. Med. Image Comput. Comput.-Assist. Intervent. (MICCAI), 2015, pp. 234–241.
S. W. Zamir, A. Arora, S. Khan, M. Hayat, F. S. Khan, and M.-H. Yang, “Restormer: Efficient transformer for high-resolution image restoration,” arXiv preprint arXiv:2111.09881, 2021.
L. Chen, X. Chu, X. Zhang, and J. Sun, “Simple baselines for image restoration,” in Lecture Notes in Computer Science, 2022, pp. 17–33, doi: 10.1007/978-3-031-20071-7_2.
I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” in Proc. Int. Conf. Learn. Represent. (ICLR), New Orleans, LA, USA, May 6–9, 2019.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in Proc. Int. Conf. Learn. Represent. (ICLR), San Diego, CA, USA, May 7–9, 2015.
A. Abdelhamed, S. Lin, and M. S. Brown, “A high-quality denoising dataset for smartphone cameras,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2018.
D. Plowman, “AI denoise training for RGB images.” Accessed: Sep. 14, 2025. [Online]. Available: https://github.com/davidplowman/denoise-rgb
Downloads
Posted
Categories
License
Copyright (c) 2025 Joseph Quan
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.