https://vilab.team/tag/%E4%BA%8B%E4%BB%B6%E9%A9%B1%E5%8A%A8/ 事件驱动 Hugo Blox Builder (https://hugoblox.com)en-usMon, 02 Feb 2026 00:00:00 +0000 https://vilab.team/media/icon_hu2896232876136423579.png https://vilab.team/tag/%E4%BA%8B%E4%BB%B6%E9%A9%B1%E5%8A%A8/ https://vilab.team/publication/evtexture-event-driven-texture-enhancement-for-video-super-r/ Mon, 02 Feb 2026 00:00:00 +0000 https://vilab.team/publication/evtexture-event-driven-texture-enhancement-for-video-super-r/ <p>本文提出EvTexture++，一种事件驱动的视频超分辨率纹理增强框架。与以往将事件用于运动估计不同，该方法利用事件的高频时空细节显式恢复纹理，通过定制纹理增强分支和迭代纹理增强模块，逐步挖掘高时间分辨率事件信息，实现纹理区域的渐进细化，从而生成更精确、细节更丰富的高分辨率视频。该框架还可作为即插即用模块提升现有VSR模型性能。</p> https://vilab.team/event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-ieee-tpami/ Mon, 02 Feb 2026 00:00:00 +0000 https://vilab.team/event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-ieee-tpami/ <p>热烈祝贺开大纯同学！论文《EvTexture++: Event-Driven Texture Enhancement for Video Super-Resolution》已发表在 <em>IEEE TPAMI</em>。</p> <h2 id="evtexture-event-driven-texture-enhancement-for-video-super-resolution">EvTexture++: Event-Driven Texture Enhancement for Video Super-Resolution</h2> <p><strong>祝贺开大纯同学！该论文已发表在 <em>IEEE TPAMI</em>。</strong></p> <p> <figure > <div class="d-flex justify-content-center"> <div class="w-100" ><img alt="EvTexture&#43;&#43;: Event-Driven Texture Enhancement for Video Super-Resolution" srcset=" /event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-ieee-tpami/images/paper-01_hu133827975853785128.webp 400w, /event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-ieee-tpami/images/paper-01_hu10222371703703860375.webp 760w, /event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-ieee-tpami/images/paper-01_hu15191880168499112270.webp 1200w" src="https://vilab.team/event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-ieee-tpami/images/paper-01_hu133827975853785128.webp" width="760" height="348" loading="lazy" data-zoomable /></div> </div></figure> </p> <ul> <li><strong>作者：</strong> Dachun Kai、Jiayao Lu、Yueyi Zhang、Xiaoyan Sun</li> <li><strong>发表载体：</strong> <em>IEEE TPAMI</em></li> <li><strong>发表时间：</strong> 2026年2月2日</li> <li><strong>相关链接：</strong> <a href="https://ieeexplore.ieee.org/abstract/document/11369964/" target="_blank" rel="noopener">论文链接</a> · <a href="https://arxiv.org/pdf/2606.13580" target="_blank" rel="noopener">PDF</a> · <a href="https://github.com/DachunKai/EvTexture" target="_blank" rel="noopener">代码</a></li> </ul> <h3 id="论文介绍">论文介绍</h3> <p>本文提出EvTexture++，一种事件驱动的视频超分辨率纹理增强框架。与以往将事件用于运动估计不同，该方法利用事件的高频时空细节显式恢复纹理，通过定制纹理增强分支和迭代纹理增强模块，逐步挖掘高时间分辨率事件信息，实现纹理区域的渐进细化，从而生成更精确、细节更丰富的高分辨率视频。该框架还可作为即插即用模块提升现有VSR模型性能。</p> https://vilab.team/publication/deep-spiking-unet-for-image-processing/ Sun, 01 Jan 2023 00:00:00 +0000 https://vilab.team/publication/deep-spiking-unet-for-image-processing/ <p>本文提出一种深度脉冲U-Net架构，将脉冲神经网络的生物合理性与U-Net的多尺度特征提取能力相结合，用于图像处理任务。通过脉冲神经元替代传统激活函数，在保持图像处理性能的同时显著降低计算能耗，为低功耗边缘端图像处理提供了新思路。</p>