{"title":"利用波束倾斜测量和环境细胞观测中的深度学习降低膜源噪声。","authors":"Fumiaki Ichihashi, Yoshio Takahashi, Toshiaki Tanigaki","doi":"10.1093/jmicro/dfaf031","DOIUrl":null,"url":null,"abstract":"<p><p>Electron microscopy using an environmental cell is a powerful tool for observing catalysts and other nanomaterials in gases and liquids. An environmental cell must contain amorphous silicon-nitride membranes because they protect the sample environment from the vacuum of the electron microscope and enable the electron beam to pass through the cell. However, the membranes superimpose non-uniform contrast on the projected image, degrading image quality. We propose a method for removing the noise derived from the membranes using Noise2Noise, a deep-learning method, for a series of transmission-electron-microscope images with slight electron-beam tilt and evaluated its effectiveness. We succeeded in removing the membrane-derived noise while retaining the information of the sample in the cell. We also succeeded in efficiently removing Poisson noise. We believe this method will enable measurements requiring high signal-to-noise ratios, which could previously only be observed in a vacuum, to be conducted in an environmental cell.</p>","PeriodicalId":74193,"journal":{"name":"Microscopy (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":1.9000,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Reduction of Membrane-derived Noise Using Beam-tilt Measurement and Deep Learning in Observation using Environmental Cell.\",\"authors\":\"Fumiaki Ichihashi, Yoshio Takahashi, Toshiaki Tanigaki\",\"doi\":\"10.1093/jmicro/dfaf031\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Electron microscopy using an environmental cell is a powerful tool for observing catalysts and other nanomaterials in gases and liquids. An environmental cell must contain amorphous silicon-nitride membranes because they protect the sample environment from the vacuum of the electron microscope and enable the electron beam to pass through the cell. However, the membranes superimpose non-uniform contrast on the projected image, degrading image quality. We propose a method for removing the noise derived from the membranes using Noise2Noise, a deep-learning method, for a series of transmission-electron-microscope images with slight electron-beam tilt and evaluated its effectiveness. We succeeded in removing the membrane-derived noise while retaining the information of the sample in the cell. We also succeeded in efficiently removing Poisson noise. We believe this method will enable measurements requiring high signal-to-noise ratios, which could previously only be observed in a vacuum, to be conducted in an environmental cell.</p>\",\"PeriodicalId\":74193,\"journal\":{\"name\":\"Microscopy (Oxford, England)\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2025-06-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Microscopy (Oxford, England)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1093/jmicro/dfaf031\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Microscopy (Oxford, England)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/jmicro/dfaf031","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Reduction of Membrane-derived Noise Using Beam-tilt Measurement and Deep Learning in Observation using Environmental Cell.
Electron microscopy using an environmental cell is a powerful tool for observing catalysts and other nanomaterials in gases and liquids. An environmental cell must contain amorphous silicon-nitride membranes because they protect the sample environment from the vacuum of the electron microscope and enable the electron beam to pass through the cell. However, the membranes superimpose non-uniform contrast on the projected image, degrading image quality. We propose a method for removing the noise derived from the membranes using Noise2Noise, a deep-learning method, for a series of transmission-electron-microscope images with slight electron-beam tilt and evaluated its effectiveness. We succeeded in removing the membrane-derived noise while retaining the information of the sample in the cell. We also succeeded in efficiently removing Poisson noise. We believe this method will enable measurements requiring high signal-to-noise ratios, which could previously only be observed in a vacuum, to be conducted in an environmental cell.