EvTexture++: Event-Driven Texture Enhancement for Video Super-Resolution

Abstract:Event-based vision has drawn increasing attention owing to its distinctive properties, including ultra-high temporal resolution and extreme dynamic range. Recent works have introduced it to video super-resolution (VSR) to enhance flow estimation and temporal alignment. In contrast, this paper shifts the focus of event signals from motion refinement to texture enhancement in VSR. We propose EvTexture++, the first event-driven framework dedicated to texture enhancement in VSR. It leverages high-frequency spatiotemporal details from events to improve texture recovery. EvTexture++ incorporates a customized texture enhancement branch, along with an iterative texture enhancement module that progressively exploits high-temporal-resolution event information for texture restoration. This enables gradual refinement of texture regions across iterations, yielding more accurate and detailed high-resolution outputs. Besides intra-frame texture recovery, large motions could degrade inter-frame temporal consistency, particularly in texture regions, leading to texture flickering. To mitigate this, we further exploit the continuous-time motion cues of events to enhance temporal consistency, introducing a temporal texture alignment module that estimates event-guided texture-aware flow for precise inter-frame texture alignment. Moreover, EvTexture++ is designed as a plug-and-play tool to flexibly boost the performance of existing VSR models. Experiments on five datasets demonstrate that EvTexture++ achieves state-of-the-art performance. When integrated into recent VSR models, it yields significant improvements, with gains of up to 1.55 dB in PSNR on the texture-rich Vid4 dataset. Code: this https URL.

IEEE TPAMI 2026. Extended version of arXiv:2406.13457 (ICML 2024). Project page: this https URL

Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)

arXiv:2606.13580 [cs.CV]

(or arXiv:2606.13580v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2606.13580 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 48, no. 6, pp. 6642-6659, June 2026

https://doi.org/10.1109/TPAMI.2026.3660020 Focus to learn more DOI(s) linking to related resources

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