Denoising Low-Dose CT Images Using a Multi-Layer Convolutional Analysis-Based Sparse Encoder Network

Imaging in the field of low-dose computed tomography (LDCT) tend to be rather noisy and artificial but is diagnostically useful. One approach to improve the quality of LDCT images is to use deep learning (DL) techniques. DL-based methods produce state-of-the-art performance in low-level medical imag...

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Bibliographic Details

Published in:	2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) pp. 1 - 6
Main Authors:	Kang, Yanqin, Liu, Jin, Liu, Tao, Qiang, Jun
Format:	Conference Proceeding
Language:	English
Published:	IEEE 05.11.2022
Subjects:	Computed tomography Computer aided software engineering Convolution Convolutional codes CT image denoise Image edge detection noise-artifacts residual network sparse encoder Uncertainty Visualization
Online Access:	Get full text
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