[口头报告]Preliminary studies on Dual-energy CT image super-resolution based on dual-dictionary learning - 报告详情

Preliminary studies on Dual-energy CT image super-resolution based on dual-dictionary learning
编号:68 稿件编号:47 访问权限:仅限参会人 更新:2021-10-30 18:52:25 浏览:203次 口头报告

报告开始:2021年11月14日 14:45 (Asia/Shanghai)

报告时间:15min

所在会议:[PS1] Plenary Session 1 [CT1] Workshop on CT

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摘要
 

Abstract—This paper proposes a dual-energy computed tomography (DECT) image super-resolution (SR) reconstruction scheme. The SR reconstruction scheme is based on sparse representation theory and dictionary learning of low-resolution and high-resolution image block pairs to improve the poor quality of low-resolution dual-energy CT images obtained in clinical practice. The proposed strategy is based on the idea of sparse representation, that is, image blocks can be well represented by sparse coding elements from over-complete dictionaries. We have jointly trained two pairs of dictionaries for high spectral CT images and low spectral CT images, and each pair of dictionaries contains dictionaries of low-resolution and high-resolution image blocks. Low-resolution dual-energy CT images can be represented by a low-resolution dictionary trained on high spectral CT images and a dictionary trained on low spectral CT images multiplied by sparse representation coefficients. And the sparse representation coefficient is multiplied by the corresponding high-resolution dictionary to reconstruct the high-resolution CT images. With an appropriate amount of iterative operations, the reconstructed high-resolution image can obtain better image quality and clearer visual effects. Experiments prove that the dual-energy CT image reconstructed by the sparse representation method has improved peak signal-to-noise ratio and structural similarity, and the image details and textures are clearer.
关键字
super-resolution,dual-dictionary learning,sparse representation,dual-energy computed tomography
报告人
Xinyi Zhong
Information Engineering University

稿件作者
Xinyi Zhong Information Engineering University
Ailong Cai Information Engineering University
Ningning Liang Information Engineering University
Xiaohuan Yu Information Engineering University
Lei Li Information Engineering University
Bin Yan Information Engineering University
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重要日期

摘要提交日期:

2021/08/31

2021/10/25

全文投稿日期:  

2021/09/15

2021/10/25

录取通知日期: 

2021/09/30

2021/11/01

会议日期:   2021-11-12-2021-11-14

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