1 Ocak 2018 Dergi makalesi Açık Erişim

Isil, Cagatay; Yorulmaz, Mustafa; Solmaz, Berkan; Turhan, Adil Burak; Yurdakul, Celalettin; Unlu, Selim; Ozbay, Ekmel; Koc, Aykut

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        "affiliation": "ASELSAN Res Ctr, TR-06370 Ankara, Turkey", 
        "name": "Yorulmaz, Mustafa"
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        "affiliation": "ASELSAN Res Ctr, TR-06370 Ankara, Turkey", 
        "name": "Solmaz, Berkan"
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        "affiliation": "Bilkent Univ, NANOTAM Nanotechnol Res Ctr, TR-06800 Ankara, Turkey", 
        "name": "Turhan, Adil Burak"
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        "affiliation": "Boston Univ, Dept Elect & Comp Engn, Boston, MA 02215 USA", 
        "name": "Yurdakul, Celalettin"
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    "description": "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", 
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