超分辨率matlab代码-HAN:我们的ECCV2020论文“通过整体注意力网络实现单图像超分辨率”的PyTorch代码

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超分辨率matlab代码韩 我们的ECCV 2020论文“通过整体注意力网络实现单图像超分辨率”的PyTorch代码 该存储库适用于以下论文中介绍的HAN 张玉伦,李坤鹏,李凯,王丽晨,钟斌能和付云,“通过整体注意力网络实现单图像超分辨率”,ECCV 2020, 该代码基于RCAN(PyTorch)构建并在具有Titan X / 1080Ti / Xp GPU的Ubuntu 16.04 / 18.04环境(Python3.6,PyTorch_0.4.0,CUDA8.0,cuDNN5.1)上进行了测试。 内容 介绍 信息功能在单图像超分辨率任务中起着至关重要的作用。 事实证明,渠道关注对于保留每一层中信息量丰富的功能是有效的。 但是,频道注意力将每个卷积层视为一个单独的过程,从而错过了不同层之间的相关性。 为了解决这个问题,我们提出了一个新的整体注意网络(HAN),该网络由一个图层注意模块(LAM)和一个通道空间注意模块(CSAM)组成,以对图层,通道和位置之间的整体相互依赖性进行建模。 具体地,提出的LAM通过考虑各层之间的相关性来自适应地强调分层特征。 同时,CSAM学习每个通道所有

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