EvTexture++: Event-Based Vision Framework Enhances Video Super-Resolution Through Texture Recovery
Researchers have developed EvTexture++, a new framework that uses event-based vision sensors to improve video super-resolution by focusing on texture enhancement rather than motion refinement. Event cameras capture high-frequency spatiotemporal details that traditional cameras miss, enabling better recovery of fine details and reduced flickering across frames. The approach achieves state-of-the-art results and can be integrated into existing video super-resolution models as a plug-and-play tool.
EvTexture++ represents a novel application of event-based vision to video super-resolution, shifting the focus from motion estimation to texture enhancement. Event cameras offer ultra-high temporal resolution and extreme dynamic range, properties that the framework leverages through a customized texture enhancement branch and iterative refinement module. The system progressively exploits high-temporal-resolution event information to restore texture details within frames, while also addressing inter-frame temporal consistency issues that cause texture flickering through an event-guided texture-aware flow estimation module. Tested on five datasets, EvTexture++ demonstrates state-of-the-art performance, with improvements up to 1.55 dB in PSNR on texture-rich content. The framework's plug-and-play design allows it to enhance existing video super-resolution models without requiring complete retraining.
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- arXiv cs.AICenter
EvTexture++: Event-Driven Texture Enhancement for Video Super-Resolution
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