<?xml version="1.0" encoding="utf-8" standalone="yes" ?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
  <channel>
    <title>图像去噪 | ViLab</title>
    <link>https://vilab.team/tag/%E5%9B%BE%E5%83%8F%E5%8E%BB%E5%99%AA/</link>
      <atom:link href="https://vilab.team/tag/%E5%9B%BE%E5%83%8F%E5%8E%BB%E5%99%AA/index.xml" rel="self" type="application/rss+xml" />
    <description>图像去噪</description>
    <generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Fri, 14 Jun 2024 00:00:00 +0000</lastBuildDate>
    <image>
      <url>https://vilab.team/media/icon_hu2896232876136423579.png</url>
      <title>图像去噪</title>
      <link>https://vilab.team/tag/%E5%9B%BE%E5%83%8F%E5%8E%BB%E5%99%AA/</link>
    </image>
    
    <item>
      <title>Deep multi-threshold spiking-UNet for image processing</title>
      <link>https://vilab.team/publication/deep-multi-threshold-spiking-unet-for-image-processing/</link>
      <pubDate>Fri, 14 Jun 2024 00:00:00 +0000</pubDate>
      <guid>https://vilab.team/publication/deep-multi-threshold-spiking-unet-for-image-processing/</guid>
      <description>&lt;p&gt;本文提出Spiking-UNet，将脉冲神经网络与U-Net架构相结合用于图像处理任务。针对脉冲传播导致的信息损失问题，设计多阈值脉冲神经元以增强信息传递能力；同时采用基于预训练U-Net的转换与微调训练策略，有效解决了训练难题。在图像分割和去噪等任务上验证了所提方法的有效性。&lt;/p&gt;
</description>
    </item>
    
  </channel>
</rss>
