Super-resolution reconstruction of vertebrate microfossil computed tomography images based on deep learning

doi:10.1002/xrs.3389

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	Super-resolution reconstruction of vertebrate microfossil computed tomography images based on deep learning
	Hou, Yemao 1; Canul-Ku, Mario 2; Cui, Xindong 3; Zhu, Min1,4,5
	2023-08-02
发表期刊	X-RAY SPECTROMETRY
ISSN	0049-8246
期号	0 页码:10
摘要	Micropaleontologists use the fine structures of microfossils to extract evolutionary information. These structures could not be directly observed with the naked eye. Recently, paleontologists resort to computed tomography (CT) images to mine the information, and pursue higher resolution CT images with in-depth research. Therefore, we propose a new model, weighted super-resolution generative adversarial network (WSRGAN), for the super-resolution reconstruction of CT images. The model proposed herein (WSRGAN) obtained higher LPIPS (0.0757) on the experimental dataset, compared with Bilinear (0.4289), Bicubic (0.4166), EDSR (0.2281), WDSR (0.2640), and SRGAN (0.0815). WSRGAN meets the requirements of paleontologists for reconstructing fish microfossils. We hope that more super-resolution reconstruction methods based on deep learning could be applied to paleontology and achieve better performance.
关键词	CT image deep learning microfossil super-resolution reconstruction WSRGAN
DOI	10.1002/xrs.3389
关键词[WOS]	GENERATIVE ADVERSARIAL NETWORKS ; RESOLUTION
收录类别	SCI
语种	英语
资助项目	Strategic Priority Research Program of Chinese Academy of Sciences[XDB26000000] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA19050102] ; National Natural Science Foundation of China[42130209]
WOS研究方向	Spectroscopy
WOS类目	Spectroscopy
WOS记录号	WOS:001039638700001
出版者	WILEY
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.205/handle/311034/22785
专题	中国科学院古脊椎动物与古人类研究所
通讯作者	Zhu, Min
作者单位	1.Chinese Acad Sci, Inst Vertebrate Paleontol & Paleoanthropol, Key Lab Vertebrate Evolut & Human Origins, Beijing, Peoples R China 2.Virtual Univ State Guanajuato, Guanajuato, Mexico 3.Peking Univ, Sch Earth & Space Sci, Key Lab Orogen Belts & Crustal Evolut, Beijing, Peoples R China 4.Univ Chinese Acad Sci, Coll Earth & Planetary Sci, Beijing, Peoples R China 5.Chinese Acad Sci, Inst Vertebrate Paleontol & Paleoanthropol, Key Lab Vertebrate Evolut & Human Origins, Beijing 100044, Peoples R China
推荐引用方式 GB/T 7714	Hou, Yemao,Canul-Ku, Mario,Cui, Xindong,et al. Super-resolution reconstruction of vertebrate microfossil computed tomography images based on deep learning[J]. X-RAY SPECTROMETRY,2023(0):10.
APA	Hou, Yemao,Canul-Ku, Mario,Cui, Xindong,&Zhu, Min.(2023).Super-resolution reconstruction of vertebrate microfossil computed tomography images based on deep learning.X-RAY SPECTROMETRY(0),10.
MLA	Hou, Yemao,et al."Super-resolution reconstruction of vertebrate microfossil computed tomography images based on deep learning".X-RAY SPECTROMETRY .0(2023):10.

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