{"title":"Super‐resolution reconstruction of vertebrate microfossil computed tomography images based on deep learning","authors":"Yemao Hou, Mario Canul‐Ku, Xindong Cui, Min Zhu","doi":"10.1002/xrs.3389","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":23867,"journal":{"name":"X-Ray Spectrometry","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"X-Ray Spectrometry","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.1002/xrs.3389","RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"SPECTROSCOPY","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍：
X-Ray Spectrometry is devoted to the rapid publication of papers dealing with the theory and application of x-ray spectrometry using electron, x-ray photon, proton, γ and γ-x sources.
Covering advances in techniques, methods and equipment, this established journal provides the ideal platform for the discussion of more sophisticated X-ray analytical methods.
Both wavelength and energy dispersion systems are covered together with a range of data handling methods, from the most simple to very sophisticated software programs. Papers dealing with the application of x-ray spectrometric methods for structural analysis are also featured as well as applications papers covering a wide range of areas such as environmental analysis and monitoring, art and archaelogical studies, mineralogy, forensics, geology, surface science and materials analysis, biomedical and pharmaceutical applications.
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