{"id":{"repo_id":"aachen","oai_identifier":"oai:publications.rwth-aachen.de:61872"},"canonical_url":"https://search.dev.ndltd.org/etd/aachen/oai:publications.rwth-aachen.de:61872","repository":{"repo_id":"aachen","name":"RWTH Aachen University","base_url":"https://publications.rwth-aachen.de/oai2d"},"display":{"title":"Advanced data acquisition and exploitation in scanning near-field (magneto-)optical microscopy","abstract":"Scanning near-field optical microscopy (SNOM) is a versatile imaging technique that combines the well-known advantages of optical microscopy with a resolution beyond the classical diffraction limit. The development of new contrast mechanisms, detection geometries and tip fabrication methods has been pursued very actively during the recent years. The occurrence and suppression of artifacts are also a subject of intense discussion and research. However, gaining information by microscopy is a two-step process. The physical data acquisition itself is only the first step; an analysis of the obtained data is required to form an image that visualizes the desired information. While it is state of the art to employ sophisticated computational methods for the analysis in domains like astronomy, harnessing that power for SNOM remains a niche field. This dissertation proposes an integrated framework that combines improvements of the physical data acquisition with novel computational data exploitation methods to produce improved SNOM images. Using an existing magneto-optical SNOM system as an object of study, all the processing stages that the raw data undergo on their way from the sample to the image are reconsidered. Access is provided to the actual raw data, of which only a far less informative condensate was previously available for further processing. For magneto-optical measurements, a crosstalk of the sample topography into the magneto-optical image is demonstrated experimentally, and a method to produce topography-free magnetic test samples is subsequently developed. Improved strategies are provided for the processing of the raw data into a first raw image. To better suppress high-frequency noise, novel de-noising methods based on wavelet filtering are developed. They allow for a far better compromise between feature retention and filtering efficiency than the previously used Fourier filtering. To increase the resolution of the de-noised image beyond the usual practical limit of the aperture used for the imaging, a concept for image deconvolution is developed. Using both simulated and actual SNOM images, it is established how much can be gained in image quality by the combination of de-noising and deconvolution and how this gain depends on the purity of the initial raw data. It is also discussed to which extent de-noising and deconvolution can be applied to other types of microscopy. Furthermore, it is demonstrated that de-noising and deconvolution not only improve images, but also supply novel approaches for improving the physical data acquisition. E.g., a novel detection mode may exploit the dither motion of the tip, which is always present for the purpose of distance regulation, for an advantageous modulation of the optical signal.","abstract_html":"Scanning near-field optical microscopy (SNOM) is a versatile imaging technique that combines the well-known advantages of optical microscopy with a resolution beyond the classical diffraction limit. The development of new contrast mechanisms, detection geometries and tip fabrication methods has been pursued very actively during the recent years. The occurrence and suppression of artifacts are also a subject of intense discussion and research. However, gaining information by microscopy is a two-step process. The physical data acquisition itself is only the first step; an analysis of the obtained data is required to form an image that visualizes the desired information. While it is state of the art to employ sophisticated computational methods for the analysis in domains like astronomy, harnessing that power for SNOM remains a niche field. This dissertation proposes an integrated framework that combines improvements of the physical data acquisition with novel computational data exploitation methods to produce improved SNOM images. Using an existing magneto-optical SNOM system as an object of study, all the processing stages that the raw data undergo on their way from the sample to the image are reconsidered. Access is provided to the actual raw data, of which only a far less informative condensate was previously available for further processing. For magneto-optical measurements, a crosstalk of the sample topography into the magneto-optical image is demonstrated experimentally, and a method to produce topography-free magnetic test samples is subsequently developed. Improved strategies are provided for the processing of the raw data into a first raw image. To better suppress high-frequency noise, novel de-noising methods based on wavelet filtering are developed. They allow for a far better compromise between feature retention and filtering efficiency than the previously used Fourier filtering. To increase the resolution of the de-noised image beyond the usual practical limit of the aperture used for the imaging, a concept for image deconvolution is developed. Using both simulated and actual SNOM images, it is established how much can be gained in image quality by the combination of de-noising and deconvolution and how this gain depends on the purity of the initial raw data. It is also discussed to which extent de-noising and deconvolution can be applied to other types of microscopy. Furthermore, it is demonstrated that de-noising and deconvolution not only improve images, but also supply novel approaches for improving the physical data acquisition. E.g., a novel detection mode may exploit the dither motion of the tip, which is always present for the purpose of distance regulation, for an advantageous modulation of the optical signal.","abstract_has_math":false,"creators":["Kiendl, Fabian"],"institution":"Publikationsserver der RWTH Aachen University","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Güntherodt, Gernot"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2005,"date_issued":"2005","date_published":"2005","updated_at":"2026-07-30T19:43:19Z","subjects":["info:eu-repo/classification/ddc/530","Optische Nahfeldmikroskopie","Bildverarbeitung","Physik","Rasternahfeldmikroskopie","Wavelets","Entfaltung"],"languages":["eng"],"rights":["info:eu-repo/semantics/openAccess"],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["https://publications.rwth-aachen.de/search?p=id:%22RWTH-CONV-123487%22"],"render_values":[{"text":"https://publications.rwth-aachen.de/search?p=id:%22RWTH-CONV-123487%22","href":"https://publications.rwth-aachen.de/search?p=id:%22RWTH-CONV-123487%22","code":true}]}]},"links":{"outbound_url":"https://publications.rwth-aachen.de/record/61872","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Güntherodt, Gernot"]},{"key":"dc:creator","label":"Author","values":["Kiendl, Fabian"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:coverage","label":"Dc Coverage","values":["DE"]},{"key":"dc:date","label":"Dc Date","values":["2005"]},{"key":"dc:publisher","label":"Institution","values":["Publikationsserver der RWTH Aachen University"]},{"key":"dc:relation","label":"Dc Relation","values":["info:eu-repo/semantics/altIdentifier/urn/urn:nbn:de:hbz:82-20050693"]},{"key":"dc:type","label":"Dc Type","values":["info:eu-repo/semantics/doctoralThesis","info:eu-repo/semantics/publishedVersion"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["info:eu-repo/classification/ddc/530","Optische Nahfeldmikroskopie","Bildverarbeitung","Physik","Rasternahfeldmikroskopie","Wavelets","Entfaltung"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["info:eu-repo/semantics/openAccess"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://publications.rwth-aachen.de/record/61872","https://publications.rwth-aachen.de/search?p=id:%22RWTH-CONV-123487%22"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Scanning near-field optical microscopy (SNOM) is a versatile imaging technique that combines the well-known advantages of optical microscopy with a resolution beyond the classical diffraction limit. The development of new contrast mechanisms, detection geometries and tip fabrication methods has been pursued very actively during the recent years. The occurrence and suppression of artifacts are also a subject of intense discussion and research. However, gaining information by microscopy is a two-step process. The physical data acquisition itself is only the first step; an analysis of the obtained data is required to form an image that visualizes the desired information. While it is state of the art to employ sophisticated computational methods for the analysis in domains like astronomy, harnessing that power for SNOM remains a niche field. This dissertation proposes an integrated framework that combines improvements of the physical data acquisition with novel computational data exploitation methods to produce improved SNOM images. Using an existing magneto-optical SNOM system as an object of study, all the processing stages that the raw data undergo on their way from the sample to the image are reconsidered. Access is provided to the actual raw data, of which only a far less informative condensate was previously available for further processing. For magneto-optical measurements, a crosstalk of the sample topography into the magneto-optical image is demonstrated experimentally, and a method to produce topography-free magnetic test samples is subsequently developed. Improved strategies are provided for the processing of the raw data into a first raw image. To better suppress high-frequency noise, novel de-noising methods based on wavelet filtering are developed. They allow for a far better compromise between feature retention and filtering efficiency than the previously used Fourier filtering. To increase the resolution of the de-noised image beyond the usual practical limit of the aperture used for the imaging, a concept for image deconvolution is developed. Using both simulated and actual SNOM images, it is established how much can be gained in image quality by the combination of de-noising and deconvolution and how this gain depends on the purity of the initial raw data. It is also discussed to which extent de-noising and deconvolution can be applied to other types of microscopy. Furthermore, it is demonstrated that de-noising and deconvolution not only improve images, but also supply novel approaches for improving the physical data acquisition. E.g., a novel detection mode may exploit the dither motion of the tip, which is always present for the purpose of distance regulation, for an advantageous modulation of the optical signal."]},{"key":"dc:source","label":"Dc Source","values":["Aachen : Publikationsserver der RWTH Aachen University 122 S. : Ill., graph. Darst. 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The physical data acquisition itself is only the first step; an analysis of the obtained data is required to form an image that visualizes the desired information. While it is state of the art to employ sophisticated computational methods for the analysis in domains like astronomy, harnessing that power for SNOM remains a niche field. This dissertation proposes an integrated framework that combines improvements of the physical data acquisition with novel computational data exploitation methods to produce improved SNOM images. Using an existing magneto-optical SNOM system as an object of study, all the processing stages that the raw data undergo on their way from the sample to the image are reconsidered. Access is provided to the actual raw data, of which only a far less informative condensate was previously available for further processing. For magneto-optical measurements, a crosstalk of the sample topography into the magneto-optical image is demonstrated experimentally, and a method to produce topography-free magnetic test samples is subsequently developed. Improved strategies are provided for the processing of the raw data into a first raw image. To better suppress high-frequency noise, novel de-noising methods based on wavelet filtering are developed. They allow for a far better compromise between feature retention and filtering efficiency than the previously used Fourier filtering. To increase the resolution of the de-noised image beyond the usual practical limit of the aperture used for the imaging, a concept for image deconvolution is developed. Using both simulated and actual SNOM images, it is established how much can be gained in image quality by the combination of de-noising and deconvolution and how this gain depends on the purity of the initial raw data. It is also discussed to which extent de-noising and deconvolution can be applied to other types of microscopy. Furthermore, it is demonstrated that de-noising and deconvolution not only improve images, but also supply novel approaches for improving the physical data acquisition. E.g., a novel detection mode may exploit the dither motion of the tip, which is always present for the purpose of distance regulation, for an advantageous modulation of the optical signal."],"dc:identifier":["https://publications.rwth-aachen.de/record/61872","https://publications.rwth-aachen.de/search?p=id:%22RWTH-CONV-123487%22"],"dc:language":["eng"],"dc:publisher":["Publikationsserver der RWTH Aachen University"],"dc:relation":["info:eu-repo/semantics/altIdentifier/urn/urn:nbn:de:hbz:82-20050693"],"dc:rights":["info:eu-repo/semantics/openAccess"],"dc:source":["Aachen : Publikationsserver der RWTH Aachen University 122 S. : Ill., graph. Darst. (2005). = Aachen, Techn. Hochsch., Diss., 2005"],"dc:subject":["info:eu-repo/classification/ddc/530","Optische Nahfeldmikroskopie","Bildverarbeitung","Physik","Rasternahfeldmikroskopie","Wavelets","Entfaltung"],"dc:title":["Advanced data acquisition and exploitation in scanning near-field (magneto-)optical microscopy"],"dc:type":["info:eu-repo/semantics/doctoralThesis","info:eu-repo/semantics/publishedVersion"]},"updated_at":"2026-07-30T19:43:19Z"}