Resolution Enhancement Of Wide-Field Interferometric Microscopy By Coupled Deep Autoencoders
| Autor: | Berkan Solmaz, Celalettin Yurdakul, Aykut Koc, Ekmel Ozbay, Çağatay Işıl, Mustafa Yorulmaz, Adil Burak Turhan, Selim Unlu |
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| Přispěvatelé: | Özbay, Ekmel, Turhan, Adil Burak |
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Microscope Image quality Computer science business.industry Resolution (electron density) Image processing Interferometric microscopy 01 natural sciences Wide field Atomic and Molecular Physics and Optics law.invention 010309 optics 03 medical and health sciences 030104 developmental biology Transformation (function) Optics Optical microscope law 0103 physical sciences Computer vision Artificial intelligence Electrical and Electronic Engineering business Engineering (miscellaneous) |
| Zdroj: | Applied Optics |
| Popis: | Wide-field interferometric microscopy is a highly sensitive, label-free, and low-cost biosensing imaging technique capable of visualizing individual biological nanoparticles such as viral pathogens and exosomes. However, further resolution enhancement is necessary to increase detection and classification accuracy of subdiffraction-limited nanoparticles. In this study, we propose a deep-learning approach, based on coupled deep autoencoders, to improve resolution of images of L-shaped nanostructures. During training, our method utilizes microscope image patches and their corresponding manual truth image patches in order to learn the transformation between them. Following training, the designed network reconstructs denoised and resolution-enhanced image patches for unseen input. (c) 2018 Optical Society of America |
| Databáze: | OpenAIRE |
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