@article{ART003130924},
author={Seoyun Jang and Dong Hoon Lim},
title={Medical Image Denoising using Wavelet Transform-Based CNN Model},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2024},
volume={29},
number={10},
pages={21-34}
TY - JOUR
AU - Seoyun Jang
AU - Dong Hoon Lim
TI - Medical Image Denoising using Wavelet Transform-Based CNN Model
JO - Journal of The Korea Society of Computer and Information
PY - 2024
VL - 29
IS - 10
PB - The Korean Society Of Computer And Information
SP - 21
EP - 34
SN - 1598-849X
AB - In medical images such as MRI(Magnetic Resonance Imaging) and CT(Computed Tomography) images, noise removal has a significant impact on the performance of medical imaging systems.
Recently, the introduction of deep learning in image processing technology has improved the performance of noise removal methods. However, there is a limit to removing only noise while preserving details in the image domain. In this paper, we propose a wavelet transform-based CNN(Convolutional Neural Network) model, namely the WT-DnCNN(Wavelet Transform-Denoising Convolutional Neural Network) model, to improve noise removal performance. This model first removes noise by dividing the noisy image into frequency bands using wavelet transform, and then applies the existing DnCNN model to the corresponding frequency bands to finally remove noise. In order to evaluate the performance of the WT-DnCNN model proposed in this paper, experiments were conducted on MRI and CT images damaged by various noises, namely Gaussian noise, Poisson noise, and speckle noise. The performance experiment results show that the WT-DnCNN model is superior to the traditional filter, i.e., the BM3D(Block-Matching and 3D Filtering) filter, as well as the existing deep learning models, DnCNN and CDAE(Convolution Denoising AutoEncoder) model in qualitative comparison, and in quantitative comparison, the PSNR(Peak Signal-to-Noise Ratio) and SSIM(Structural Similarity Index Measure) values were 36~43 and 0.93~0.98 for MRI images and 38~43 and 0.95~0.98 for CT images, respectively. In addition, in the comparison of the execution speed of the models, the DnCNN model was much less than the BM3D model, but it took a long time due to the addition of the wavelet transform in the comparison with the DnCNN model.
KW - Deep learning;Denoising Convolutional Neural Network;Medical Image;Noise Reduction;Wavelet Transform
DO -
UR -
ER -
Seoyun Jang and Dong Hoon Lim. (2024). Medical Image Denoising using Wavelet Transform-Based CNN Model. Journal of The Korea Society of Computer and Information, 29(10), 21-34.
Seoyun Jang and Dong Hoon Lim. 2024, "Medical Image Denoising using Wavelet Transform-Based CNN Model", Journal of The Korea Society of Computer and Information, vol.29, no.10 pp.21-34.
Seoyun Jang, Dong Hoon Lim "Medical Image Denoising using Wavelet Transform-Based CNN Model" Journal of The Korea Society of Computer and Information 29.10 pp.21-34 (2024) : 21.
Seoyun Jang, Dong Hoon Lim. Medical Image Denoising using Wavelet Transform-Based CNN Model. 2024; 29(10), 21-34.
Seoyun Jang and Dong Hoon Lim. "Medical Image Denoising using Wavelet Transform-Based CNN Model" Journal of The Korea Society of Computer and Information 29, no.10 (2024) : 21-34.
Seoyun Jang; Dong Hoon Lim. Medical Image Denoising using Wavelet Transform-Based CNN Model. Journal of The Korea Society of Computer and Information, 29(10), 21-34.
Seoyun Jang; Dong Hoon Lim. Medical Image Denoising using Wavelet Transform-Based CNN Model. Journal of The Korea Society of Computer and Information. 2024; 29(10) 21-34.
Seoyun Jang, Dong Hoon Lim. Medical Image Denoising using Wavelet Transform-Based CNN Model. 2024; 29(10), 21-34.
Seoyun Jang and Dong Hoon Lim. "Medical Image Denoising using Wavelet Transform-Based CNN Model" Journal of The Korea Society of Computer and Information 29, no.10 (2024) : 21-34.
