Autoencoders, Kernels, and Multilayer Perceptrons for Electron Micrograph Restoration and Compression

TL;DR

This paper introduces a comprehensive set of autoencoders, kernels, and multilayer perceptrons designed for the restoration and compression of electron micrographs across various microscopy techniques and compression ratios.

Contribution

It provides a large collection of trained models and methods specifically tailored for electron micrograph restoration and compression, including detailed training configurations for different microscopy modalities.

Findings

Autoencoders effectively compress electron micrographs at multiple ratios.

Kernels and MLPs can approximate the denoising effects of autoencoders.

Models are available for diverse microscopy techniques and compression levels.

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