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@article{ART002942372},
author={SeongEonSeung and HANSUNGHYUN and Ji Hye Heo and Dong Hoon Lim},
title={Efficient CT Image Denoising Using Deformable Convolutional AutoEncoder Model},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2023},
volume={28},
number={3},
pages={25-33},
doi={10.9708/jksci.2023.28.03.025}

TY - JOUR
AU - SeongEonSeung
AU - HANSUNGHYUN
AU - Ji Hye Heo
AU - Dong Hoon Lim
TI - Efficient CT Image Denoising Using Deformable Convolutional AutoEncoder Model
JO - Journal of The Korea Society of Computer and Information
PY - 2023
VL - 28
IS - 3
PB - The Korean Society Of Computer And Information
SP - 25
EP - 33
SN - 1598-849X
AB - Noise generated during the acquisition and transmission of CT images acts as a factor that degrades image quality. Therefore, noise removal to solve this problem is an important preprocessing process in image processing. In this paper, we remove noise by using a deformable convolutional autoencoder (DeCAE) model in which deformable convolution operation is applied instead of the existing convolution operation in the convolutional autoencoder (CAE) model of deep learning. Here, the deformable convolution operation can extract features of an image in a more flexible area than the conventional convolution operation. The proposed DeCAE model has the same encoder-decoder structure as the existing CAE model, but the encoder is composed of deformable convolutional layers and the decoder is composed of conventional convolutional layers for efficient noise removal. To evaluate the performance of the DeCAE model proposed in this paper, experiments were conducted on CT images corrupted by various noises, that is, Gaussian noise, impulse noise, and Poisson noise. As a result of the performance experiment, the DeCAE model has more qualitative and quantitative measures than the traditional filters, that is, the Mean filter, Median filter, Bilateral filter and NL-means method, as well as the existing CAE models, that is, MAE (Mean Absolute Error), PSNR (Peak Signal-to-Noise Ratio) and SSIM. (Structural Similarity Index Measure) showed excellent results.
KW - DeCAE;deep learning;deformable convolution;CT image;noise reduction
DO - 10.9708/jksci.2023.28.03.025
ER -

SeongEonSeung, HANSUNGHYUN, Ji Hye Heo and Dong Hoon Lim. (2023). Efficient CT Image Denoising Using Deformable Convolutional AutoEncoder Model. Journal of The Korea Society of Computer and Information, 28(3), 25-33.

SeongEonSeung, HANSUNGHYUN, Ji Hye Heo and Dong Hoon Lim. 2023, "Efficient CT Image Denoising Using Deformable Convolutional AutoEncoder Model", Journal of The Korea Society of Computer and Information, vol.28, no.3 pp.25-33. Available from: doi:10.9708/jksci.2023.28.03.025

SeongEonSeung, HANSUNGHYUN, Ji Hye Heo, Dong Hoon Lim "Efficient CT Image Denoising Using Deformable Convolutional AutoEncoder Model" Journal of The Korea Society of Computer and Information 28.3 pp.25-33 (2023) : 25.

SeongEonSeung, HANSUNGHYUN, Ji Hye Heo, Dong Hoon Lim. Efficient CT Image Denoising Using Deformable Convolutional AutoEncoder Model. 2023; 28(3), 25-33. Available from: doi:10.9708/jksci.2023.28.03.025

SeongEonSeung, HANSUNGHYUN, Ji Hye Heo and Dong Hoon Lim. "Efficient CT Image Denoising Using Deformable Convolutional AutoEncoder Model" Journal of The Korea Society of Computer and Information 28, no.3 (2023) : 25-33.doi: 10.9708/jksci.2023.28.03.025

SeongEonSeung; HANSUNGHYUN; Ji Hye Heo; Dong Hoon Lim. Efficient CT Image Denoising Using Deformable Convolutional AutoEncoder Model. Journal of The Korea Society of Computer and Information, 28(3), 25-33. doi: 10.9708/jksci.2023.28.03.025

SeongEonSeung; HANSUNGHYUN; Ji Hye Heo; Dong Hoon Lim. Efficient CT Image Denoising Using Deformable Convolutional AutoEncoder Model. Journal of The Korea Society of Computer and Information. 2023; 28(3) 25-33. doi: 10.9708/jksci.2023.28.03.025

SeongEonSeung, HANSUNGHYUN, Ji Hye Heo, Dong Hoon Lim. Efficient CT Image Denoising Using Deformable Convolutional AutoEncoder Model. 2023; 28(3), 25-33. Available from: doi:10.9708/jksci.2023.28.03.025

SeongEonSeung, HANSUNGHYUN, Ji Hye Heo and Dong Hoon Lim. "Efficient CT Image Denoising Using Deformable Convolutional AutoEncoder Model" Journal of The Korea Society of Computer and Information 28, no.3 (2023) : 25-33.doi: 10.9708/jksci.2023.28.03.025