Description of the activity
Computational microscopy is at the intersection of computer science and optics, and extends the capabilities of the classical optical microscopy with the help of computation. In particular, the complexity of imaging systems moves from the optical arrangements to the computational schemes, which mainly consists into the smart manipulation of raw data to achieve high-quality imaging capabilities. The research activity is focused on the design, development, and implementation of advanced image processing methods and machine learning frameworks for pushing the capabilities and performance beyond the limits of existing optical systems. The main topics carried out by the research group cover the fields of Advanced holographic processing, Super-resolution imaging, Field of View extension, phase retrieval, 3D tomography, learning approaches for image classification and fast processing. Moreover, the group is active in the design and the setting up of unconventional compact microscopy devices for point-of-care diagnostics and environmental monitoring at the single cell/particle level.
KEYWORDS: quantitative phase imaging, digital holography, Fourier Ptychographic microscopy, tomographic flow cytometry, flow-scanning holographic cytometry, lensless in-line microscopy, polarization-resolved holographic microscopy, advanced image processing, machine learning, deep learning.
P. Memmolo | V. Bianco | L. Miccio | P. Ferraro | J. Behal | Z. Wang | D. Pirone | D. Sirico | M. Valentino
National and International Collaborations
- École Polytechnique Fédérale de Lausanne (EPFL),
- University of California Los Angeles (UCLA),
- Tel Aviv University,
- LeMans University,
- University of Connecticut (UCONN),
- Tsinghua University,
- Università degli Studi di Salerno (UNISA),
- Università degli Studi di Napoli “Federico II” (UNINA),
- Università di Bologna (UNIBO),
- CEINGE – Biotecnologie Avanzate,
- Stazione Zoologica Anton Dohrn.
- Ptychography Labs;
- Digital Holography Labs
Active projects and contracts
- Project PRIN 2017, Morphological Biomarkers for early diagnosis in Oncology (MORFEO). Prot. 2017N7R2CJ.
- Joint research project for Scientific and Technological Cooperation Italy-Israel 2021, Deep-learning classification of dynamically flowing circulating tumors cells imaged by quantitative phase microscopy (Deep-Class-CTCs).