Abstract
We present 14 autoencoders, 15 kernels and 14 multilayer perceptrons for electron micrograph restoration and compression. These have been trained for transmission electron microscopy (TEM), scanning transmission electron microscopy (STEM) and for both (TEM+STEM). TEM autoencoders have been trained for 1$\times$, 4$\times$, 16$\times$ and 64$\times$ compression, STEM autoencoders for 1$\times$, 4$\times$ and 16$\times$ compression and TEM+STEM autoencoders for 1$\times$, 2$\times$, 4$\times$, 8$\times$, 16$\times$, 32$\times$ and 64$\times$ compression. Kernels and multilayer perceptrons have been trained to approximate the denoising effect of the 4$\times$ compression autoencoders. Kernels for input sizes of 3, 5, 7, 11 and 15 have been fitted for TEM, STEM and TEM+STEM. TEM multilayer perceptrons have been trained with 1 hidden layer for input sizes of 3, 5 and 7 and with 2 hidden layers for input sizes of 5 and 7. STEM multilayer perceptrons have been trained with 1 hidden layer for input sizes of 3, 5 and 7. TEM+STEM multilayer perceptrons have been trained with 1 hidden layer for input sizes of 3, 5, 7 and 11 and with 2 hidden layers for input sizes of 3 and 7. Our code, example usage and pre-trained models are available at https://github.com/Jeffrey-Ede/Denoising-Kernels-MLPs-Autoencoders
Abstract (translated)
我们提供了14个自动编码器，15个内核和14个多层感知器，用于电子显微照片的恢复和压缩。这些已经过传输电子显微镜（TEM），扫描透射电子显微镜（STEM）和两者（TEM + STEM）的培训。 TEM自动编码器已经培训了1 $ \次$，4 $ \次$，16 $ \次$和64 $ \次$压缩，STEM自动编码器1 $ \次$，4 $ \次$和16 $ \次$ compression和TEM + STEM自动编码器1 $ \次$，2 $ \次$，4 $ \次$，8 $ \次$，16 $ \次$，32 $ \次$和64 $ \次$压缩。已经训练了内核和多层感知器以接近4 $ \次$压缩自动编码器的去噪效果。输入尺寸为3,5,7,11和15的内核适用于TEM，STEM和TEM + STEM。 TEM多层感知器已经训练了1个隐藏层，输入大小为3,5和7，并且有2个隐藏层，输入大小为5和7. STEM多层感知器已经训练了1个隐藏层，输入大小为3,5和7. TEM + STEM多层感知器已经过培训，其中1个隐藏层用于输入大小为3,5,7和11，并且具有2个隐藏层，用于输入大小为3和7.我们的代码，示例用法和预训练模型可用在https://github.com/Jeffrey-Ede/Denoising-Kernels-MLPs-Autoencoders
URL
https://arxiv.org/abs/1808.09916
