Adaptive Wavelet Transform Techniques for Optimal Noise Reduction in Computed Tomography Scans
Received: 6 October 2025 | Revised: 8 November 2025 and 7 December 2025 | Accepted: 9 December 2025 | Online: 9 February 2026
Corresponding author: Lassaad Ben Ammar
Abstract
Computed Tomography (CT) often suffers from an inevitable increase in image noise due to the pursuit of reduced dose radiation, which compromises diagnostic quality and makes the detection of subtle features challenging. Conventional noise reduction methods often face a trade-off between effectively suppressing noise and preserving crucial fine-scale anatomical details and sharp edges. This paper presents an advanced denoising approach using the Adaptive Wavelet Transform (AWT) to obtain optimal noise reduction in CT scans while maintaining high image fidelity. The proposed method employs the multi-resolution decomposition property of the wavelet transform to separate noise (typically residing in high-frequency detail coefficients) from image structures (present across all sub-bands). An adaptive thresholding strategy is introduced to adjust the coefficient shrinkage based on the Genetic Algorithm (GA), thereby ensuring an effective balance between noise suppression and structural detail preservation. The proposed approach was rigorously evaluated on a publicly available dataset, comparing its performance with standard fixed-threshold wavelet methods and conventional spatial domain filters. Quantitative results demonstrate that the proposed method achieves superior PSNR and SSIM while preserving fine structural information that is vital for clinical diagnosis.
Keywords:
discrete wavelet transform, adaptive threshold optimization, image denoising, adaptive algorithmDownloads
References
M. Diwakar and M. Kumar, "A review on CT image noise and its denoising," Biomedical Signal Processing and Control, vol. 42, pp. 73–88, Apr. 2018. DOI: https://doi.org/10.1016/j.bspc.2018.01.010
S. V. M. Sagheer and S. N. George, "A review on medical image denoising algorithms," Biomedical Signal Processing and Control, vol. 61, Aug. 2020, Art. no. 102036. DOI: https://doi.org/10.1016/j.bspc.2020.102036
R. Kaur, M. Juneja, and A. K. Mandal, "A comprehensive review of denoising techniques for abdominal CT images," Multimedia Tools and Applications, vol. 77, no. 17, pp. 22735–22770, Sept. 2018. DOI: https://doi.org/10.1007/s11042-017-5500-5
A. M. L. Lanzolla, G. Andria, F. Attivissimo, G. Cavone, M. Spadavecchia, and T. Magli, "Denoising filter to improve the quality of CT images," in 2009 IEEE Instrumentation and Measurement Technology Conference, Singapore, May 2009, pp. 947–950. DOI: https://doi.org/10.1109/IMTC.2009.5168588
A. A. Omer, O. I. Hassan, A. I. Ahmed, and A. Abdelrahman, "Denoising CT Images using Median based Filters: a Review," in 2018 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE), Khartoum, Sudan, Aug. 2018, pp. 1–6. DOI: https://doi.org/10.1109/ICCCEEE.2018.8515829
D. Bhonsle, V. Chandra, and G. R. Sinha, "Medical Image Denoising Using Bilateral Filter," International Journal of Image, Graphics and Signal Processing, vol. 4, no. 6, pp. 36–43, July 2012. DOI: https://doi.org/10.5815/ijigsp.2012.06.06
Riya, B. Gupta, and S. S. Lamba, "An efficient anisotropic diffusion model for image denoising with edge preservation," Computers & Mathematics with Applications, vol. 93, pp. 106–119, July 2021. DOI: https://doi.org/10.1016/j.camwa.2021.03.029
M. Diwakar and P. Singh, "CT image denoising using multivariate model and its method noise thresholding in non-subsampled shearlet domain," Biomedical Signal Processing and Control, vol. 57, Mar. 2020, Art. no. 101754. DOI: https://doi.org/10.1016/j.bspc.2019.101754
M. Diwakar, P. Singh, and D. Garg, "Edge-guided filtering based CT image denoising using fractional order total variation," Biomedical Signal Processing and Control, vol. 92, June 2024, Art. no. 106072. DOI: https://doi.org/10.1016/j.bspc.2024.106072
S. Katta, P. Singh, D. Garg, V. Ravi, and M. Diwakar, "A Dual CT Image Denoising Approach using Guided Filter and method-based Noise in the NSST Domain," The Open Bioinformatics Journal.
L. Gabralla, H. Mahersia, and M. Zaroug, "Denoising CT Images using wavelet transform," International Journal of Advanced Computer Science and Applications, vol. 6, no. 5, 2015. DOI: https://doi.org/10.14569/IJACSA.2015.060520
J. Isabona, A. L. Imoize, and S. Ojo, "Image Denoising based on Enhanced Wavelet Global Thresholding Using Intelligent Signal Processing Algorithm," International Journal of Image, Graphics and Signal Processing, vol. 15, no. 5, pp. 1–16, Oct. 2023. DOI: https://doi.org/10.5815/ijigsp.2023.05.01
A. Dixit, and P. Sharma, "A Comparative Study of Wavelet Thresholding for Image Denoising," International Journal of Image, Graphics and Signal Processing, vol. 6, no. 12, pp. 39–46, Nov. 2014. DOI: https://doi.org/10.5815/ijigsp.2014.12.06
L. B. Ammar, "Enhanced Diagnosis of Lung Cancer through an Ensemble Learning Model leveraging an Adaptive Optimization Algorithm," Engineering, Technology & Applied Science Research, vol. 14, no. 6, pp. 18518–18524, Dec. 2024. DOI: https://doi.org/10.48084/etasr.9096
P. Singh and A. Shankar, "A novel optical image denoising technique using convolutional neural network and anisotropic diffusion for real-time surveillance applications," Journal of Real-Time Image Processing, vol. 18, no. 5, pp. 1711–1728, Oct. 2021. DOI: https://doi.org/10.1007/s11554-020-01060-0
C. Tian, L. Fei, W. Zheng, Y. Xu, W. Zuo, and C. W. Lin, "Deep learning on image denoising: An overview," Neural Networks, vol. 131, pp. 251–275, Nov. 2020. DOI: https://doi.org/10.1016/j.neunet.2020.07.025
S. Izadi, D. Sutton, and G. Hamarneh, "Image denoising in the deep learning era," Artificial Intelligence Review, vol. 56, no. 7, pp. 5929–5974, July 2023. DOI: https://doi.org/10.1007/s10462-022-10305-2
Z. Li, S. Zhou, J. Huang, L. Yu, and M. Jin, "Investigation of Low-Dose CT Image Denoising Using Unpaired Deep Learning Methods," IEEE Transactions on Radiation and Plasma Medical Sciences, vol. 5, no. 2, pp. 224–234, Mar. 2021. DOI: https://doi.org/10.1109/TRPMS.2020.3007583
R. T. Sadia, J. Chen, and J. Zhang, "CT image denoising methods for image quality improvement and radiation dose reduction," Journal of Applied Clinical Medical Physics, vol. 25, no. 2, 2024, Art. no. e14270. DOI: https://doi.org/10.1002/acm2.14270
H. Alyasriy and A. Muayed, "The IQ-OTH/NCCD lung cancer dataset." Kaggle, [Online]. Available: https://www.kaggle.com/datasets/hamdallak/the-iqothnccd-lung-cancer-dataset.
T. K. Abuya, R. M. Rimiru, G. O. Okeyo, T. K. Abuya, R. M. Rimiru, and G. O. Okeyo, "An Image Denoising Technique Using Wavelet-Anisotropic Gaussian Filter-Based Denoising Convolutional Neural Network for CT Images," Applied Sciences, vol. 13, no. 21, Nov. 2023. DOI: https://doi.org/10.3390/app132112069
H. Zhao et al., "Low Dose CT Image Denoising: A Comparative Study of Deep Learning Models and Training Strategies," AI Medicine, vol. 1, no. 1, Nov. 2024, Art. no. 7. DOI: https://doi.org/10.53941/aim.2024.100007
J. Zhang, Z. Shangguan, W. Gong, and Y. Cheng, "A novel denoising method for low-dose CT images based on transformer and CNN," Computers in Biology and Medicine, vol. 163, Sept. 2023, Art. no. 107162. DOI: https://doi.org/10.1016/j.compbiomed.2023.107162
Downloads
How to Cite
License
Copyright (c) 2025 Lassaad Ben Ammar
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
