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Pietro Ferraro

Researcher at National Research Council

Publications -  720
Citations -  14634

Pietro Ferraro is an academic researcher from National Research Council. The author has contributed to research in topics: Digital holography & Holography. The author has an hindex of 61, co-authored 653 publications receiving 12666 citations. Previous affiliations of Pietro Ferraro include Aeritalia & Centre national de la recherche scientifique.

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Optofluidic holographic microscopy with custom field of view (FoV) using a linear array detector

TL;DR: A coherent 3D microscopy approach with a holographic modality that is specifically suitable for studying biological samples while they simply flow along microfluidic paths is presented, named here as Space-Time Digital Hologram (STDH).
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Quantitative phase imaging trends in biomedical applications

TL;DR: This review identifies four different trends of relevance in the context of biomedical applications, namely QPI-cytometry, development of point-of-care devices, tomographic phase reconstruction techniques and learning-based approaches, providing a current insight in the way they are shifting the authors' approach to the acquisition and treatment of QPI data.
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Holographic microscope slide in a spatio-temporal imaging modality for reliable 3D cell counting.

TL;DR: A novel platform based on the compactness of a holographic microscope slide in combination with the new computational features of space-time digital holography (STDH) that uses a 1D linear sensor array (LSA) instead of 2D CCD or CMOS cameras to respond to real diagnostic needs is proposed.
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In situ investigation of periodic poling in congruent LiNbO3 by quantitative interference microscopy

TL;DR: In this article, the phase shift distribution across domain walls is measured by measuring the variation of the optical phase shift distributions across domains, and phase map movies of the evolving domains are presented and discussed.
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Three-dimensional color object visualization and recognition using multi-wavelength computational holography.

TL;DR: It is shown that a small number of training images is sufficient for the color object classification, and principal component analysis and mixture discriminant analysis analyze multi-spectral information in the reconstructed images.