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

文献类型：期刊论文

作者Hou, Yemao5; Canul-Ku, Mario4; Cui, Xindong3; Zhu, Min1,2,5
刊名X-RAY SPECTROMETRY
出版日期2023-08-02
页码10
ISSN号0049-8246
关键词CT image deep learning microfossil super-resolution reconstruction WSRGAN
DOI10.1002/xrs.3389
通讯作者Zhu, Min(zhumin@ivpp.ac.cn)
英文摘要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.
WOS关键词GENERATIVE ADVERSARIAL NETWORKS ; RESOLUTION
资助项目National Natural Science Foundation of China[42130209] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA19050102] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB26000000]
WOS研究方向Spectroscopy
语种英语
出版者WILEY
WOS记录号WOS:001039638700001
资助机构National Natural Science Foundation of China ; Strategic Priority Research Program of Chinese Academy of Sciences
源URL[http://119.78.100.205/handle/311034/22784]  
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 100044, Peoples R China
2.Univ Chinese Acad Sci, Coll Earth & Planetary Sci, Beijing, Peoples R China
3.Peking Univ, Sch Earth & Space Sci, Key Lab Orogen Belts & Crustal Evolut, Beijing, Peoples R China
4.Virtual Univ State Guanajuato, Guanajuato, Mexico
5.Chinese Acad Sci, Inst Vertebrate Paleontol & Paleoanthropol, Key Lab Vertebrate Evolut & Human Origins, Beijing, 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,2023:10.
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,10.
MLA Hou, Yemao,et al."Super-resolution reconstruction of vertebrate microfossil computed tomography images based on deep learning".X-RAY SPECTROMETRY (2023):10.

入库方式： OAI收割

来源：古脊椎动物与古人类研究所

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