数据融合matlab代码-AWSRN:我们论文“具有自适应加权学习网络的轻型图像超分辨率”的PyTorch代码

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数据融合matlab代码自适应加权学习网络的轻量图像超分辨率 王朝峰,李振和石军,“具有自适应加权学习网络的轻量图像超分辨率”, 该代码基于 依存关系 的Python 3.5 PyTorch> = 0.4.0 麻木 skimage 意象 matplotlib tqdm 代码 git clone git@github.com:ChaofWang/AWSRN.git cd AWSRN 抽象的 近年来,深度学习已以出色的性能成功地应用于单图像超分辨率(SISR)任务。 但是,大多数基于卷积神经网络的SR模型都需要大量计算,这限制了它们在现实世界中的应用。 在这项工作中,为SISR提出了一种轻量级SR网络,称为自适应加权超分辨率网络(AWSRN),以解决此问题。 在AWSRN中设计了一种新颖的局部融合块(LFB),用于有效的残差学习,它由堆叠的自适应加权残差单元(AWRU)和局部残差融合单元(LRFU)组成。 此外,提出了一种自适应加权多尺度(AWMS)模块,以充分利用重建层中的特征。 AWMS由几个不同的尺度卷积组成,并且可以根据AWMS中针对轻量级网络的自适应权重的贡献来删除冗余尺度分

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