图像超分辨率::magnifying_glass_tilted_right:可以对图像进行超缩放,并使用残差密集网络和对抗网络进行实验-源码

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图像超分辨率(ISR) 该项目的目标是扩大和提高低分辨率图像的质量。 该项目包含针对单一图像超分辨率(ISR)的各种残差密集网络的Keras实现,以及使用内容和对抗性损失组件来训练这些网络的脚本。 已实施的网络包括: 残差密集网络中描述的超规模残差密集网络(Zhang et al.2018) 网络中描述的残留致密网络中的超规模残留(Wang等人,2018) Keras VGG19网络的多输出版本,用于感知损失中的深度特征提取 一种自定义判别器网络,基于(SRGANS,Ledig et al.2017)中的描述 阅读完整的文档,为: : 。 和进行培训和预测。 此外,我们提供了一些脚本,以简化AWS和在云上的培训,仅需少量命令。 ISR与Python 3.6兼容,并在Apache 2.0许可下分发。 我们欢迎任何形式的贡献。 如果您想贡献,请参阅部分。 内容 预训练网络 创建模型对象时,可直接获得用于生成这些图像的权重。 当前有4种型号可用: RDN:较大的psnr,较小的psnr,取消噪声 RRDN:甘斯 用法示例: model = RRDN(weights=

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