{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/autoencoders-kernels-and-multilayer","title":"Autoencoders, Kernels, and Multilayer Perceptrons for Electron Micrograph Restoration and Compression","arxiv_id":"1808.09916","date":"2018-08-29","proceeding":null,"authors":["Jeffrey M. Ede"],"abstract":"We present 14 autoencoders, 15 kernels and 14 multilayer perceptrons for\nelectron micrograph restoration and compression. These have been trained for\ntransmission electron microscopy (TEM), scanning transmission electron\nmicroscopy (STEM) and for both (TEM+STEM). TEM autoencoders have been trained\nfor 1$\\times$, 4$\\times$, 16$\\times$ and 64$\\times$ compression, STEM\nautoencoders for 1$\\times$, 4$\\times$ and 16$\\times$ compression and TEM+STEM\nautoencoders for 1$\\times$, 2$\\times$, 4$\\times$, 8$\\times$, 16$\\times$,\n32$\\times$ and 64$\\times$ compression. Kernels and multilayer perceptrons have\nbeen trained to approximate the denoising effect of the 4$\\times$ compression\nautoencoders. Kernels for input sizes of 3, 5, 7, 11 and 15 have been fitted\nfor TEM, STEM and TEM+STEM. TEM multilayer perceptrons have been trained with 1\nhidden layer for input sizes of 3, 5 and 7 and with 2 hidden layers for input\nsizes of 5 and 7. STEM multilayer perceptrons have been trained with 1 hidden\nlayer for input sizes of 3, 5 and 7. TEM+STEM multilayer perceptrons have been\ntrained with 1 hidden layer for input sizes of 3, 5, 7 and 11 and with 2 hidden\nlayers for input sizes of 3 and 7. Our code, example usage and pre-trained\nmodels are available at\nhttps://github.com/Jeffrey-Ede/Denoising-Kernels-MLPs-Autoencoders","url_abs":"http://arxiv.org/abs/1808.09916v1","url_pdf":"http://arxiv.org/pdf/1808.09916v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"autoencoders-kernels-and-multilayer","repo_url":"https://github.com/Jeffrey-Ede/Denoising-Kernels-MLPs-Autoencoders","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}