EvTexture++: Event-Based Vision Framework Enhances Video Super-Resolution Through Texture Recovery
Researchers have published EvTexture++, an event-driven framework that uses event camera signals to enhance texture recovery in video super-resolution (VSR). Unlike prior event-based VSR methods that focused on motion estimation, EvTexture++ targets high-frequency texture detail and temporal consistency. The work, accepted to IEEE TPAMI 2026, demonstrates gains of up to 1.55 dB in PSNR on a standard texture-rich benchmark and is designed as a plug-and-play module for existing VSR models.
EvTexture++, developed by Dachun Kai and colleagues, is presented as the first event-driven framework specifically dedicated to texture enhancement in video super-resolution rather than motion refinement. The system exploits the ultra-high temporal resolution and wide dynamic range of event cameras to recover fine spatial detail that conventional frame-based methods struggle to reconstruct. A core component is an iterative texture enhancement module that progressively refines texture regions across multiple passes, while a temporal texture alignment module uses continuous-time motion cues from events to reduce texture flickering caused by large inter-frame motions. The framework is designed as a plug-and-play addition, meaning it can be integrated into existing VSR pipelines without requiring full retraining from scratch. Experiments across five datasets show state-of-the-art performance, with up to 1.55 dB PSNR improvement on the Vid4 dataset when combined with recent VSR models. The paper is an extended version of work originally presented at ICML 2024 and is published in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 48, no. 6, June 2026.
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- arXiv cs.AICenter
EvTexture++: Event-Driven Texture Enhancement for Video Super-Resolution
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