做视频超分或事件视觉的团队，EvTexture++ 的即插即用设计可以直接集成到现有模型提升纹理细节，值得在纹理密集场景试试。
EvTexture++ 是首个专注于视频超分辨率（VSR）中纹理增强的事件驱动框架，利用事件相机的高频时空细节提升纹理恢复质量。该框架包含定制纹理增强分支和迭代纹理增强模块，逐步利用高时间分辨率事件信息进行纹理修复，生成更精确的高分辨率输出。针对大运动导致的纹理闪烁问题，EvTexture++ 引入时间纹理对齐模块，利用事件连续时间运动线索实现精确帧间纹理对齐。该框架设计为即插即用工具，可灵活提升现有 VSR 模型性能，在纹理丰富的 Vid4 数据集上 PSNR 提升高达 1.55 dB。实验表明 EvTexture++ 在五个数据集上达到最先进水平，代码已开源。
EvTexture++: Event-Driven Texture Enhancement for Video Super-Resolution
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: https://github.com/DachunKai/EvTexture.