{"653309":{"#nid":"653309","#data":{"type":"event","title":"Ph.D. Dissertation Defense - Lee Richert","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle\u003C\/strong\u003E\u003Cem\u003E:\u0026nbsp; \u003C\/em\u003E\u003Cem\u003EMulti-Layer Dictionary Learning Using Low-Rank Updates\u003C\/em\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ECommittee:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. David Anderson, ECE, Chair, Advisor\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Mark Davenport, ECE\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Justin Romberg, ECE\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Ashwin Pananjady, ECE\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Jacob Abernethy, CoC\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract:\u0026nbsp;\u003C\/strong\u003EThis dissertation presents a novel convolutional dictionary learning algorithm for signals with a large number of channels. The algorithm uses low-rank updates for the dictionary, so that a matrix decomposition necessary for pursuit can be updated efficiently. The novel approach is applied to multi-layer dictionary models with multi-channel dictionaries and single-channel coefficients and demonstrated on the task of JPEG compression artifact removal.\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Multi-Layer Dictionary Learning Using Low-Rank Updates "}],"uid":"28475","created_gmt":"2021-12-01 21:47:31","changed_gmt":"2021-12-01 21:47:31","author":"Daniela Staiculescu","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2021-12-15T11:30:00-05:00","event_time_end":"2021-12-15T13:30:00-05:00","event_time_end_last":"2021-12-15T13:30:00-05:00","gmt_time_start":"2021-12-15 16:30:00","gmt_time_end":"2021-12-15 18:30:00","gmt_time_end_last":"2021-12-15 18:30:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"434381","name":"ECE Ph.D. Dissertation Defenses"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"},{"id":"1808","name":"graduate students"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}