{"id":{"repo_id":"ttu","oai_identifier":"oai:ttu-ir.tdl.org:2346/16830"},"canonical_url":"https://search.dev.ndltd.org/etd/ttu/oai:ttu-ir.tdl.org:2346/16830","repository":{"repo_id":"ttu","name":"Texas Technology University","base_url":"https://ttu-ir.tdl.org/server/oai/request"},"display":{"title":"Advanced techniques for digital image processing","abstract":"A new algorithm for enhancing a degraded grey scale image is proposed here. The enhancement algorithm is a locally adaptive Fourier filter which locates and analyzes the Fourier spectral information and then enhances the identifying features. Thus, it can achieve a better enhancement result than conventional homomorphic FFT techniques. By using a short space basis implementation, a large amount of memory space can be saved, consequently the computation speed is greatly improved. The primary objective of this algorithm is to extract linear features from a noisy image. However, the algorithm also can be modified in order to enhance other different kinds of features. The main advantages of this algorithm are: 1. It requires a small amount of computer memory; this makes it easy to implement in small computers. 2. It has fast processing speed. 3. It is powerful in extracting local linear features.","abstract_html":"A new algorithm for enhancing a degraded grey scale image is proposed here. The enhancement algorithm is a locally adaptive Fourier filter which locates and analyzes the Fourier spectral information and then enhances the identifying features. Thus, it can achieve a better enhancement result than conventional homomorphic FFT techniques. By using a short space basis implementation, a large amount of memory space can be saved, consequently the computation speed is greatly improved. The primary objective of this algorithm is to extract linear features from a noisy image. However, the algorithm also can be modified in order to enhance other different kinds of features. The main advantages of this algorithm are: 1. It requires a small amount of computer memory; this makes it easy to implement in small computers. 2. It has fast processing speed. 3. It is powerful in extracting local linear features.","abstract_has_math":false,"creators":["Tarng, Jaw-horng"],"institution":"Texas Tech University","degree_name":"M.S.E.E.","degree_level":"Masters","degree_discipline":"Electrical and Computer Engineering","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":1986,"date_issued":"1986-05","date_published":"1986-05","updated_at":"2026-07-24T05:04:51Z","subjects":["Fingerprints -- Identification","Algorithms","Computer vision","Image processing -- Digital techniques","Fourier transformations"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2346/16830","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Tarng, Jaw-horng"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2011-02-18T21:44:35Z"]},{"key":"dc:date.issued","label":"Date","values":["1986-05"]},{"key":"dc:publisher","label":"Institution","values":["Texas Tech University"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical and Computer Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S.E.E."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Texas Tech University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Fingerprints -- Identification","Algorithms","Computer vision","Image processing -- Digital techniques","Fourier transformations"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/2346/16830"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["A new algorithm for enhancing a degraded grey scale image is proposed here. The enhancement algorithm is a locally adaptive Fourier filter which locates and analyzes the Fourier spectral information and then enhances the identifying features. Thus, it can achieve a better enhancement result than conventional homomorphic FFT techniques. By using a short space basis implementation, a large amount of memory space can be saved, consequently the computation speed is greatly improved. The primary objective of this algorithm is to extract linear features from a noisy image. However, the algorithm also can be modified in order to enhance other different kinds of features. The main advantages of this algorithm are: 1. It requires a small amount of computer memory; this makes it easy to implement in small computers. 2. It has fast processing speed. 3. It is powerful in extracting local linear features."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Advanced techniques for digital image processing"]}]}],"canonical_facts":{"dc:creator":["Tarng, Jaw-horng"],"dc:date.available":["2011-02-18T21:44:35Z"],"dc:date.issued":["1986-05"],"dc:description.abstract":["A new algorithm for enhancing a degraded grey scale image is proposed here. The enhancement algorithm is a locally adaptive Fourier filter which locates and analyzes the Fourier spectral information and then enhances the identifying features. Thus, it can achieve a better enhancement result than conventional homomorphic FFT techniques. By using a short space basis implementation, a large amount of memory space can be saved, consequently the computation speed is greatly improved. The primary objective of this algorithm is to extract linear features from a noisy image. However, the algorithm also can be modified in order to enhance other different kinds of features. The main advantages of this algorithm are: 1. It requires a small amount of computer memory; this makes it easy to implement in small computers. 2. It has fast processing speed. 3. It is powerful in extracting local linear features."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["http://hdl.handle.net/2346/16830"],"dc:language.iso":["eng"],"dc:publisher":["Texas Tech University"],"dc:subject":["Fingerprints -- Identification","Algorithms","Computer vision","Image processing -- Digital techniques","Fourier transformations"],"dc:title":["Advanced techniques for digital image processing"],"dc:type":["Thesis"],"thesis:degree_discipline":["Electrical and Computer Engineering"],"thesis:degree_level":["Masters"],"thesis:degree_name":["M.S.E.E."],"thesis:institution_name":["Texas Tech University"]},"updated_at":"2026-07-24T05:04:51Z"}