BEGIN:VCALENDAR
VERSION:2.0
METHOD:PUBLISH
CALSCALE:GREGORIAN
PRODID:-//WordPress - MECv6.5.6//EN
X-ORIGINAL-URL:https://cmc.deusto.eus/
X-WR-CALNAME:cmc.deusto.eus
X-WR-CALDESC:DeustoCCM - Chair of Computational Mathematics at University of Deusto
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-PUBLISHED-TTL:PT1H
X-MS-OLK-FORCEINSPECTOROPEN:TRUE
BEGIN:VEVENT
CLASS:PUBLIC
UID:MEC-22c95b490feee0556535ea97bafb1319@cmc.deusto.eus
DTSTART:20260618T133000Z
DTEND:20260618T143000Z
DTSTAMP:20260617T041100Z
CREATED:20260617
LAST-MODIFIED:20260617
PRIORITY:5
TRANSP:OPAQUE
SUMMARY:DeustoCCM Seminar: Optimal Image Transport over Sparse Dictionaries
DESCRIPTION:Date: Thu. June 18, 2026 (15:30H)\nEvent: DeustoCCM Seminar\nResearch-Oriented\nTitle: Optimal Image Transport over Sparse Dictionaries\nSpeaker: Junqing Huang\nAffiliation: DeustoCCM, University of Deusto\nAbstract. We propose a novel optimal image transport framework over sparse dictionaries for image-to-image translation tasks by integrating the complementary strengths of Sparse Representation (SR) and Optimal Transport (OT). Specifically, we design an optimization formulation in which individual image features (e.g., color and style, etc.) are encoded via sparse representation, while an optimal transport plan is inferred over the learned dictionaries in alignment with the encoding process. Due to the compactness of sparse coding and the reduced optimal transport over sparse dictionaries, the proposed optimization model is computationally tractable and empirically efficient, thereby providing a simple yet effective paradigm for simultaneous image representation and transformation. We demonstrate its effectiveness and versatility across two representative tasks: color transform and photo-realistic style transfer, and further show its potential for image super-resolution and video-based extensions. We also conduct a series of visual comparisons and quantitative evaluations to demonstrate visually plausible and photo-realistic transfer effects, showcasing competitive performance compared with state-of-the-art methods, including deep learning models.\nWHERE\nUniversity of Deusto, DeustoTech.\nRoom: Logistar\nUnibertsitate Etorb., 24, 48007 Bilbao, Bizkaia\n
URL:https://cmc.deusto.eus/events-calendar/deustoccm-seminar-18jun2026/
ORGANIZER;CN=DeustoCCM - Chair of Computational Mathematics:MAILTO:
CATEGORIES:Events Calendar,Events Calendar Past
LOCATION:Unibertsitate Etorb., 24, 48007 Bilbao, Bizkaia
ATTACH;FMTTYPE=image/png:https://cmc.deusto.eus/wp-content/uploads/DeustoCCMseminar_jHuang_18jun2026.png
END:VEVENT
END:VCALENDAR