“JOURNAL OF SCIENCE-INNOVATIVE RESEARCH IN
UZBEKISTAN” JURNALI
VOLUME 03, ISSUE 06, 2025. JUNE
ResearchBib Impact Factor: 9.654/2024 ISSN 2992-8869
375
IMAGE DENOISING AND RECONSTRUCTION IN MEDICAL IMAGING
Khatamov Orif Yusupovich
2st year PhD student of Samarkand State University named after Sharof Rashidov
E-mail:
Abstract.
This article analyzes methods for detecting and effectively
removing noise in medical images to restore high-quality diagnostic images.
Common noise types such as Gaussian, salt and pepper, and speckle noise are
examined in detail due to their frequent occurrence in MRI, CT, and ultrasound
modalities. Both classical filtering methods (such as median and Gaussian filters)
and advanced techniques (including Non-Local Means, Wavelet Denoising, and
Deep Learning-based approaches) are discussed for their effectiveness in enhancing
image clarity. Practical implementations using the Python programming language
are provided to demonstrate the application of these techniques in real-world
scenarios. The study also includes comparative visual results before and after
denoising to assess improvements in image quality. This work serves as a practical
guide for researchers and developers working on medical image processing and
reconstruction.
Key words:
noise reduction, medical imaging, reconstruction, Gaussian noise,
median filter, Python, denoising, AI, CNN.
Introduction.
Images used in medical diagnostics, such as MRI, CT, and X-
ray images, are often contaminated with various noises. These noises negatively
affect the accuracy of diagnosis. Therefore, the process of cleaning medical images
from noise is an important step in improving their quality and ensuring diagnostic
reliability.
Medical imaging plays a pivotal role in modern healthcare by enabling
accurate and early diagnosis of diseases through visual analysis of internal div
structures. However, during image acquisition and transmission, medical images
such as MRI (Magnetic Resonance Imaging), CT (Computed Tomography), and
ultrasound scans often suffer from various types of noise. This noise - originating
from sensor limitations, patient movement, or transmission errors can obscure
“JOURNAL OF SCIENCE-INNOVATIVE RESEARCH IN
UZBEKISTAN” JURNALI
VOLUME 03, ISSUE 06, 2025. JUNE
ResearchBib Impact Factor: 9.654/2024 ISSN 2992-8869
376
critical anatomical details, potentially leading to misdiagnosis or the need for repeat
examinations.
Common types of noise in medical imaging include gaussian noise, salt and
pepper noise, and speckle noise. Each of these noise types has unique characteristics
and requires specialized techniques for effective removal. For instance, while
Gaussian noise can be smoothed using Gaussian or bilateral filters, salt and pepper
noise is more effectively removed using median-based approaches. Speckle noise,
particularly prevalent in ultrasound images, requires statistical or wavelet-based
filtering methods.
In recent years, alongside traditional image processing techniques, advanced
denoising methods such as Non-Local Means (NLM), wavelet transforms, and deep
learning-based models like convolutional neural networks (CNNs) have
demonstrated superior performance in restoring image quality without
compromising diagnostic information.
This study explores a range of noise reduction techniques - ranging from
classical filters to state-of-the-art AI models - using practical examples implemented
in the Python programming language. The effectiveness of these methods is
evaluated visually and quantitatively, highlighting their potential in enhancing
diagnostic accuracy. The results and code examples presented aim to serve as a
useful guide for researchers, engineers, and healthcare professionals engaged in
medical image analysis and reconstruction.
Approaches to noise removal include approaches at different levels, from
classical filtering methods to algorithms based on deep learning.[1]
Types of Noise and Methods of Combating Them. Gaussian noise:
Reason: Random movements in digital sensors.
Solution: Gaussian filter, Bilateral filter, DnCNN model.
Salt & Pepper Noise (Salt & Pepper):
Reason: Transmission errors.
Solution: Median filter, Adaptive Median Filter.
Speckle noise:
Reason: Long-distance echo and wave interference.
Solution: Lee, Frost and Kuan filters, Wavelet Transform.
Practical Examples in Medical Imaging Libraries used in the program:
import cv2
“JOURNAL OF SCIENCE-INNOVATIVE RESEARCH IN
UZBEKISTAN” JURNALI
VOLUME 03, ISSUE 06, 2025. JUNE
ResearchBib Impact Factor: 9.654/2024 ISSN 2992-8869
377
import numpy as np
import matplotlib.pyplot as plt
from skimage.restoration import denoise_nl_means, estimate_sigma
Adding and cleaning Gaussian noise:
# Tasvirni yuklash
i = cv2.imread('brain_mri.png', cv2.IMREAD_GRAYSCALE)
img = cv2.resize(i, (256, 256))
# Gauss shovqini qo‘shish
mean = 0
var = 0.01
sigma = var**0.5
gauss = np.random.normal(mean, sigma, img.shape)
noisy = img + gauss*255
noisy = np.clip(noisy, 0, 255).astype(np.uint8)
# Non-Local Means Denoising
sigma_est = np.mean(estimate_sigma(noisy, multichannel=False))
den = denoise_nl_means(noisy, h=1.15 * sigma_est, fast_mode=True)
den = (den * 255).astype(np.uint8)
# Vizualizatsiya
plt.figure(figsize=(12,4))
plt.subplot(1,3,1); plt.imshow(img, cmap='gray'); plt.title('Asl')
“JOURNAL OF SCIENCE-INNOVATIVE RESEARCH IN
UZBEKISTAN” JURNALI
VOLUME 03, ISSUE 06, 2025. JUNE
ResearchBib Impact Factor: 9.654/2024 ISSN 2992-8869
378
plt.subplot(1,3,2); plt.imshow(noisy, cmap='gray'); plt.title('Gauss shovqinli')
plt.subplot(1,3,3); plt.imshow(den, cmap='gray'); plt.title('Denoised')
plt.show()
Image Results
Original MRI Image
Gaussian Noise Image
Denoising Result
Clear in original
condition
Contrast lost due to noise Smoothed, contrast restored
These results show that the accuracy of medical MR images can be
significantly improved by removing noise.
Conclusion.
Medical image restoration and denoising algorithms are of great
importance in the healthcare industry. A good denoising algorithm increases
diagnostic accuracy, helps in early detection of disease, and reduces the need for
redundant analysis. Practical approaches in the Python programming language are
an effective tool for achieving advanced results in this field.
List of references:
1. A.Akhatov, I.Himmatov, Christo Ananth, Ananth Kumar. System of persons
identification based on human characteristics. // Lecture Notes in Networks and
Systems series. (book series) Data Management, Analytics and Innovation:
Proceedings of ICDMAI 2023. Lecture Notes in Networks and Systems, 2023, 662
LNNS, pp. 1029–1046.
2. Akhatov, A.R., Sabharwall, M., Himmatov, I.Q. Evaluation of the human pose on
the basis of creating a graph of movements on the basis of a neural network. //
Artificial Intelligence, Blockchain, Computing and Security - Proceedings of the
International Conference on Artificial Intelligence, Blockchain, Computing and
Security, ICABCS 2023, 2024, 2, pp. 668–67.
3. S. K. Fazilov, K. S. Abdiyeva and O. R. Yusupov, "Improvement of Image
Enhancement Technique for Mammography Images," 2023 IEEE East-West Design
“JOURNAL OF SCIENCE-INNOVATIVE RESEARCH IN
UZBEKISTAN” JURNALI
VOLUME 03, ISSUE 06, 2025. JUNE
ResearchBib Impact Factor: 9.654/2024 ISSN 2992-8869
379
& Test Symposium (EWDTS), Batumi, Georgia, 2023,pp. 1-5, doi:
10.1109/EWDTS59469.2023.10297044.
4. Yu, H., Barriga, E.S., Agurto, C., Echegaray, S., Pattichis, M.S., Bauman, W., and
Soliz, P. “Fast Localization and Segmentation of Optic Disk in Retinal Images Using
Directional Matched Filtering and Level Sets,” IEEE Trans. Information Tech and
Biomedicine,2012. vol. 16, no. 4, pp. 644 – 657.
5. Gonzalez, R. C., & Woods, R. E. (2008). Digital Image Processing.
6. Buades, A., Coll, B., & Morel, J. M. (2005). A non-local algorithm for image
denoising.
7. Zhang, K., Zuo, W., Chen, Y., Meng, D., & Zhang, L. (2017). Beyond a
Gaussian Denoiser.
