REMOVAL OF UNWANTED TERMS FROM SINGLE SHOT INLINE DIGITAL HOLOGRAMS BY CONVOLUTIONAL NEURAL NETWORK

SÜSLEYİCİ B., ESMER G. B.

Unconventional Optical Imaging IV 2024, Strasbourg, Fransa, 8 - 11 Nisan 2024, cilt.12996, (Tam Metin Bildiri) identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 12996
  • Doi Numarası: 10.1117/12.3017233
  • Basıldığı Şehir: Strasbourg
  • Basıldığı Ülke: Fransa
  • Anahtar Kelimeler: Deep Learning, Digital Holographic Microscopy, Scalar Optical Diffraction
  • Yıldız Teknik Üniversitesi Adresli: Hayır

Özet

A model to achieve high-resolution three-dimensional microscopic images from synthetically generated digital holograms by using Convolutional Neural Networks (CNNs) is proposed. By employing low-cost microscopy systems and computational techniques, we demonstrate that proposed model provides viable alternative to costly high-resolution microscopic systems. Specifically, the study focuses on the elimination of the unwanted terms in backward-propagated holograms to closely approximate original high-resolution objects.