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Open Access
Understanding Medical Image Denoising, Enhancement, and Reconstruction
1
School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India
* Corresponding Author:
Prabhishek Singh,
[email protected]
Volume 1, Issue 1
2025
Received: 06 February 2025
Accepted: 13 May 2025
Published: 26 June 2025
Article Information
Published in
Biomedical Informatics and Smart Healthcare
Volume/Issue
Volume 1, Issue 1, 2025
Pages
35-39
Cited by
1 (Crossref)
3 (Scopus)
Abstract
Medical imaging is an essential and valuable tool in modern medicine for providing essential details on the internal structures and functioning of the human body. Although very useful, the raw images obtained from medical imaging systems usually contain different types of artifacts and noise that might hide some essential diagnostic information. In this paper, the author details the conventional and non-conventional methods as well as sophisticated deep learning research methods used in improving the quality of healthcare images. The paper also delves into strategies designed to elevate the visual quality and interpretability in medical diagnostics. The paper includes some latest case studies to illustrate the usefulness of these strategies in the clinical context. This is classically imclinically importantportant because of the blend between essential information and high-level research that this review provides, thus making it an indispensable tool for students, researchers, and professionals who aim to enhance their understanding and knowledge about medical image processing technology.
Keywords
medical imaging
denoising
enhancement
reconstruction
deep learning
healthcare
Data Availability Statement
Not applicable.
Funding
This work was supported without any funding.
Conflicts of Interest
The author declare no conflicts of interest.
Ethical Approval and Consent to Participate
Not applicable.
References
- Diwakar, M., & Singh, P. (2020). CT image denoising using multivariate model and its method noise thresholding in non-subsampled shearlet domain. Biomedical Signal Processing and Control, 57, 101754.
[CrossRef] [Google Scholar] - Singh, P., Diwakar, M., Gupta, R., Kumar, S., Chakraborty, A., Bajal, E., ... & Paul, R. (2022). A method noise-based convolutional neural network technique for CT image denoising. Electronics, 11(21), 3535.
[CrossRef] [Google Scholar] - Qu, H., Liu, K., & Zhang, L. (2024). Research on improved black widow algorithm for medical image denoising. Scientific Reports, 14(1), 2514.
[CrossRef] [Google Scholar] - Annavarapu, A., & Borra, S. (2024). An adaptive watershed segmentation based medical image denoising using deep convolutional neural networks. Biomedical Signal Processing and Control, 93, 106119.
[CrossRef] [Google Scholar] - Sharif, S. M. A., Naqvi, R. A., & Loh, W. K. (2024). Two-stage deep denoising with self-guided noise attention for multimodal medical images. IEEE Transactions on Radiation and Plasma Medical Sciences.
[CrossRef] [Google Scholar] - Wen, R., Yuan, H., Ni, D., Xiao, W., & Wu, Y. (2024). From denoising training to test-time adaptation: Enhancing domain generalization for medical image segmentation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 464–474).
[CrossRef] [Google Scholar] - Du, Y., Chen, Y., Zhang, Y., & Liao, X. (2024). Research on biomedical image denoising method based on deep learning. In The 19th International Scientific and Practical Conference “Creative Business Management and Implementation of New Ideas” (p. 242). International Science Group.
[Google Scholar] - Roy, S., Bhalla, K., & Patel, R. (2024). Mathematical analysis of histogram equalization techniques for medical image enhancement: A tutorial from the perspective of data loss. Multimedia Tools and Applications, 83(5), 14363–14392.
[CrossRef] [Google Scholar] - Balaji, V., Song, T. A., Malekzadeh, M., Heidari, P., & Dutta, J. (2024). Artificial intelligence for PET and SPECT image enhancement. Journal of Nuclear Medicine, 65(1), 4–12.
[CrossRef] [Google Scholar] - Jha, K., Sakhare, A., Chavhan, N., & Lokulwar, P. P. (2024). A review on image enhancement techniques using histogram equalization. Grenze International Journal of Engineering & Technology, 10(1), 12–18.
[CrossRef] [Google Scholar] - Zhang, J., Xiao, L., Zhang, Y., Lai, J., & Yang, Y. (2024). Optimization and performance evaluation of deep learning algorithm in medical image processing. Frontiers in Computing and Intelligent Systems, 7(3), 67–71.
[Google Scholar] - Xu, J., Wu, B., Huang, J., Gong, Y., Zhang, Y., & Liu, B. (2024). Practical applications of advanced cloud services and generative AI systems in medical image analysis. arXiv preprint arXiv:2403.17549. https://arxiv.org/abs/2403.17549
[Google Scholar] - Obuchowicz, R., Strzelecki, M., & Piórkowski, A. (2024). Artificial Intelligence in Medical Imaging and Image Processing (p. 596). MDPI-Multidisciplinary Digital Publishing Institute.
[CrossRef] [Google Scholar] - Huang, J., Yang, L., Wang, F., Wu, Y., Nan, Y., Aviles-Rivero, A. I., ... & Yang, G. (2024). Mambamir: An arbitrary-masked mamba for joint medical image reconstruction and uncertainty estimation. arXiv preprint arXiv:2402.18451. https://arxiv.org/abs/2402.18451
[Google Scholar] - Zhao, H., Dong, W., Yu, R., Zhao, Z., Du, B., & Xu, Y. (2024, October). Morestyle: relax low-frequency constraint of fourier-based image reconstruction in generalizable medical image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 434-444). Cham: Springer Nature Switzerland.
[CrossRef] [Google Scholar] - Zi, Y., Wang, Q., Gao, Z., Cheng, X., & Mei, T. (2024). Research on the application of deep learning in medical image segmentation and 3D reconstruction. Academic Journal of Science and Technology, 10(2), 8–12.
[Google Scholar] - Mall, P. K., Singh, P. K., Srivastav, S., Narayan, V., Paprzycki, M., Jaworska, T., & Ganzha, M. (2023). A comprehensive review of deep neural networks for medical image processing: Recent developments and future opportunities. Healthcare Analytics, 4, 100216.
[CrossRef] [Google Scholar] - Qiu, D., Cheng, Y., & Wang, X. (2023). Medical image super-resolution reconstruction algorithms based on deep learning: A survey. Computer Methods and Programs in Biomedicine, 238, 107590.
[CrossRef] [Google Scholar] - Uppamma, P., & Bhattacharya, S. (2023). Deep learning and medical image processing techniques for diabetic retinopathy: A survey of applications, challenges, and future trends. Journal of Healthcare Engineering, 2023, 2728719.
[CrossRef] [Google Scholar]
Cited By (1)
-
M. Padma Usha, G. Kannan, Akshara Nithyasri M, Ashrath Nisha A, Keerthana L. .
2025 International Conference on Emerging Trends in Signal Processing & Computational Intelligence (ICCSPCI), 2025 .
[CrossRef]
* Citation data provided by Crossref Cited-by.
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APA Style
Singh, P. (2025). Understanding Medical Image Denoising, Enhancement, and Reconstruction. Biomedical Informatics and Smart Healthcare, 1(1), 35–39. https://doi.org/10.62762/BISH.2025.966762
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TY - JOUR AU - Singh, Prabhishek PY - 2025 DA - 2025/06/26 TI - Understanding Medical Image Denoising, Enhancement, and Reconstruction JO - Biomedical Informatics and Smart Healthcare T2 - Biomedical Informatics and Smart Healthcare JF - Biomedical Informatics and Smart Healthcare VL - 1 IS - 1 SP - 35 EP - 39 DO - 10.62762/BISH.2025.966762 UR - https://www.icck.org/article/abs/BISH.2025.966762 KW - medical imaging KW - denoising KW - enhancement KW - reconstruction KW - deep learning KW - healthcare AB - Medical imaging is an essential and valuable tool in modern medicine for providing essential details on the internal structures and functioning of the human body. Although very useful, the raw images obtained from medical imaging systems usually contain different types of artifacts and noise that might hide some essential diagnostic information. In this paper, the author details the conventional and non-conventional methods as well as sophisticated deep learning research methods used in improving the quality of healthcare images. The paper also delves into strategies designed to elevate the visual quality and interpretability in medical diagnostics. The paper includes some latest case studies to illustrate the usefulness of these strategies in the clinical context. This is classically imclinically importantportant because of the blend between essential information and high-level research that this review provides, thus making it an indispensable tool for students, researchers, and professionals who aim to enhance their understanding and knowledge about medical image processing technology. SN - 3068-5524 PB - Institute of Central Computation and Knowledge LA - English ER -
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@article{Singh2025Understand,
author = {Prabhishek Singh},
title = {Understanding Medical Image Denoising, Enhancement, and Reconstruction},
journal = {Biomedical Informatics and Smart Healthcare},
year = {2025},
volume = {1},
number = {1},
pages = {35-39},
doi = {10.62762/BISH.2025.966762},
url = {https://www.icck.org/article/abs/BISH.2025.966762},
abstract = {Medical imaging is an essential and valuable tool in modern medicine for providing essential details on the internal structures and functioning of the human body. Although very useful, the raw images obtained from medical imaging systems usually contain different types of artifacts and noise that might hide some essential diagnostic information. In this paper, the author details the conventional and non-conventional methods as well as sophisticated deep learning research methods used in improving the quality of healthcare images. The paper also delves into strategies designed to elevate the visual quality and interpretability in medical diagnostics. The paper includes some latest case studies to illustrate the usefulness of these strategies in the clinical context. This is classically imclinically importantportant because of the blend between essential information and high-level research that this review provides, thus making it an indispensable tool for students, researchers, and professionals who aim to enhance their understanding and knowledge about medical image processing technology.},
keywords = {medical imaging, denoising, enhancement, reconstruction, deep learning, healthcare},
issn = {3068-5524},
publisher = {Institute of Central Computation and Knowledge}
}
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