Applied and Computational Engineering

- The Open Access Proceedings Series for Conferences


Proceedings of the 2023 International Conference on Software Engineering and Machine Learning

Series Vol. 8 , 01 August 2023


Open Access | Article

A Critical Review on CT Image denoising in Wavelet domain

Manoj Diwakar 1 , Prabhishek Singh 2 , Achyut Shankar 3 , Sathishkumar V. E. * 4 , Kapil Joshi 5 , Mohit Kumar 6
1 Graphic Era Deemed to be University
2 Bennett University
3 University of Warwick
4 Jeonbuk National University
5 Uttaranchal University
6 Amity University Uttar Pradesh

* Author to whom correspondence should be addressed.

Applied and Computational Engineering, Vol. 8, 31-36
Published 01 August 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Manoj Diwakar, Prabhishek Singh, Achyut Shankar, Sathishkumar V. E., Kapil Joshi, Mohit Kumar. A Critical Review on CT Image denoising in Wavelet domain. ACE (2023) Vol. 8: 31-36. DOI: 10.54254/2755-2721/8/20230067.

Abstract

Medical images are commonly used today by medical practitioners for the purposes of diagnosis. For the purposes of diagnosis, diagnostic images are widely used today by medical professionals. In general, MRI works on soft tissues, and CT works on hard tissues. Due to device and hardware limitations, mathematical calculations, transition mechanisms in computers, there are chances of creating noise in medical images.In yhis paper, a critical review on CT image denoising has been performed in wavelet domain. In the transform domain, the process of removing noise from an image starts with the image or data being divided up into a representation in scale space. It has methods for setting thresholds, rules for shrinking, and a way to clean up noise based on wavelets, among other things.

Keywords

CT image denoising, wavelet transform, entropy, thresholding, PSNR, SSIM

References

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2. Elad M, Aharon M (2006) Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans Image Process 15(12):3736–45

3. Mustafa ZA, Kadah YM (2011) Multi resolution bilateral filter for MR image denoising. In: Proceeding on 1st Middle East conference on biomedical engineering (MECBME). Sharjah, pp 180–184

4. Borsdorf A, Raupach R, Flohr T, Hornegger J (2008) Wavelet based noise reduction in CT-images using correlation analysis. IEEE Trans Med Imaging 27(12):1685–1703

5. Rabbani H, Nezafat R, Gazor S (2009) Wavelet-domain medical image denoising using bivariate laplacian mixture model. IEEE Trans Biomed Eng 56(12):2826–2837

6. Blu, T., & Luisier, F. (2007). The SURE-LET approach to image denoising. IEEE Transactions on Image Processing, 16(11), 2778-2786.

7. Chen, M., Pu, Y. F., & Bai, Y. C. (2020). Low-dose CT image denoising using residual convolutional network with fractional TV loss. Neurocomputing.

8. Trung, N. T., Hoan, T. D., Trung, N. L., & Luong, M. (2020). Low-dose CT image denoising using image decomposition and sparse representation. REV Journal on Electronics and Communications, 9(3-4).

9. Li, Z., Zhou, S., Huang, J., Yu, L., & Jin, M. (2020). Investigation of low-dose CT image denoising using unpaired deep learning methods. IEEE Transactions on Radiation and Plasma Medical Sciences.

10. Kumar, B. S. (2013). Image denoising based on non-local means filter and its method noise thresholding. Signal, image and video processing, 7(6), 1211-1227.

11. Diwakar, M., Kumar, P., & Singh, A. K. (2020). CT image denoising using NLM and its method noise thresholding. Multimedia Tools and Applications, 79(21), 14449-14464.

12. Diwakar, M., Singh, P., Swarup, C., Bajal, E., Jindal, M., Ravi, V., Singh, K.U. and Singh, T., 2022. Noise Suppression and Edge Preservation for Low-Dose COVID-19 CT Images Using NLM and Method Noise Thresholding in Shearlet Domain. Diagnostics, 12(11), p.2766.

Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 2023 International Conference on Software Engineering and Machine Learning
ISBN (Print)
978-1-915371-63-8
ISBN (Online)
978-1-915371-64-5
Published Date
01 August 2023
Series
Applied and Computational Engineering
ISSN (Print)
2755-2721
ISSN (Online)
2755-273X
DOI
10.54254/2755-2721/8/20230067
Copyright
© 2023 The Author(s)
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated