{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:43:33Z","timestamp":1773801813669,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"13","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Blurry video super-resolution (BVSR) remains fundamentally ill-posed due to the simultaneous loss of high-frequency spatial details and reliable motion cues in blurry low-resolution frames. While cascade-based and joint BVSR methods struggle under severe blur, existing event-guided VSR approaches largely assume clean inputs and are ineffective against complex motion degradation. These methods fail to model blurry representations or leverage event signals for blur-aware motion cues, leading to sub-optimal performance. We propose BluR-EVSR, a unified framework that implicitly models Blurry Representations and leverages Event cameras to jointly address both blur and resolution degradation for VSR. The framework begins with a self-supervised degradation learning strategy guided by event streams and neighboring frames, enabling adaptive blur representation without requiring explicit supervision. A dynamic routing mechanism encodes spatially varying degradations, while a motion-saliency degradation-aware attention module injects motion saliency priors to facilitate efficient RGB-event fusion. Integrated into a bidirectional recurrent framework, BluR-EVSR enables temporally consistent and detail-preserving restoration with low computational cost. Extensive experiments across multiple benchmarks show that our method significantly outperforms prior BVSR and event-based approaches.<\/jats:p>","DOI":"10.1609\/aaai.v40i13.38081","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:01:20Z","timestamp":1773792080000},"page":"11032-11041","source":"Crossref","is-referenced-by-count":0,"title":["Exploiting Blurry Representations for Event-guided Video Super-Resolution"],"prefix":"10.1609","volume":"40","author":[{"given":"Zeyu","family":"Xiao","sequence":"first","affiliation":[]},{"given":"Xinchao","family":"Wang","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2026,3,14]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/38081\/42043","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/38081\/42043","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:01:21Z","timestamp":1773792081000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/38081"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i13.38081","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,14]]}}}