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Interferometry

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Micro-scale fiber-optic force sensor fabricated using direct laser writing and calibrated using machine learning.

Optics express
Fiber-optic sensors have numerous existing and emerging applications spanning areas from industrial process monitoring to medical diagnosis. Two of the most common fiber sensors are based on the fabrication of Bragg gratings or Fabry-Perot etalons. W...

Low coherence quantitative phase microscopy with machine learning model and Raman spectroscopy for the study of breast cancer cells and their classification.

Applied optics
Early-stage detection of breast cancer is the primary requirement in modern healthcare as it is the most common cancer among women worldwide. Histopathology is the most widely preferred method for the diagnosis of breast cancer, but it requires long ...

Dual-wavelength interferogram decoupling method for three-frame generalized dual-wavelength phase-shifting interferometry based on deep learning.

Journal of the Optical Society of America. A, Optics, image science, and vision
In dual-wavelength interferometry, the key issue is how to efficiently retrieve the phases at each wavelength using the minimum number of wavelength-multiplexed interferograms. To address this problem, a new dual-wavelength interferogram decoupling m...

High-resolution single-shot phase-shifting interference microscopy using deep neural network for quantitative phase imaging of biological samples.

Journal of biophotonics
White light phase-shifting interference microscopy (WL-PSIM) is a prominent technique for high-resolution quantitative phase imaging (QPI) of industrial and biological specimens. However, multiple interferograms with accurate phase-shifts are essenti...

Application of attention-DnCNN for ESPI fringe patterns denoising.

Journal of the Optical Society of America. A, Optics, image science, and vision
Fringe patterns' denoising in electronic speckle pattern interferometry (ESPI) is an important step in phase extraction. In this study, we propose a new denoising method for ESPI fringe patterns based on a convolutional neural network (CNN). The prop...

Fringe Detection and Displacement Sensing for Variable Optical Feedback-Based Self-Mixing Interferometry by Using Deep Neural Networks.

Sensors (Basel, Switzerland)
Laser feedback-based self-mixing interferometry (SMI) is a promising technique for displacement sensing. However, commercial deployment of such sensors is being held back due to reduced performance in case of variable optical feedback which invariabl...

Machine learning assisted interferometric structured illumination microscopy for dynamic biological imaging.

Nature communications
Structured Illumination Microscopy, SIM, is one of the most powerful optical imaging methods available to visualize biological environments at subcellular resolution. Its limitations stem from a difficulty of imaging in multiple color channels at onc...

Single-shot multispectral quantitative phase imaging of biological samples using deep learning.

Applied optics
Multispectral quantitative phase imaging (MS-QPI) is a high-contrast label-free technique for morphological imaging of the specimens. The aim of the present study is to extract spectral dependent quantitative information in single-shot using a highly...

Beyond Correlations: Deep Learning for Seismic Interferometry.

IEEE transactions on neural networks and learning systems
Passive seismic interferometry is a vastly generalized blind deconvolution question, where different paths through the Earth correspond to different channels called Green's functions; the sources are completely incoherent and not shared by the channe...

C-reactive protein (CRP) evaluation in human urine using optical sensor supported by machine learning.

Scientific reports
The rapid and sensitive indicator of inflammation in the human body is C-Reactive Protein (CRP). Determination of CRP level is important in medical diagnostics because, depending on that factor, it may indicate, e.g., the occurrence of inflammation o...