Multi angle

H 1 Signal processing for nonlinear diffractive imaging

Auteurs : Kamilov, Ulugbek (Auteur de la Conférence)
CIRM (Editeur )

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    Résumé : Can modern signal processing be used to overcome the diffraction limit? The classical diffraction limit states that the resolution of a linear imaging system is fundamentally limited by one half of the wavelength of light. This implies that conventional light microscopes cannot distinguish two objects placed within a distance closer than 0.5 × 400 = 200nm (blue) or 0.5 × 700 = 350nm (red). This significantly impedes biomedical discovery by restricting our ability to observe biological structure and processes smaller than 100nm. Recent progress in sparsity-driven signal processing has created a powerful paradigm for increasing both the resolution and overall quality of imaging by promoting model-based image acquisition and reconstruction. This has led to multiple influential results demonstrating super-resolution in practical imaging systems. To date, however, the vast majority of work in signal processing has neglected the fundamental nonlinearity of the object-light interaction and its potential to lead to resolution enhancement. As a result, modern theory heavily focuses on linear measurement models that are truly effective only when object-light interactions are weak. Without a solid signal processing foundation for understanding such nonlinear interactions, we undervalue their impact on information transfer in the image formation. This ultimately limits our capability to image a large class of objects, such as biological tissue, that generally are in large-volumes and interact strongly and nonlinearly with light.
    The goal of this talk is to present the recent progress in model-based imaging under multiple scattering. We will discuss several key applications including optical diffraction tomography, Fourier Ptychography, and large-scale Holographic microscopy. We will show that all these application can benefit from models, such as the Rytov approximation and beam propagation method, that take light scattering into account. We will discuss the integration of such models into the state-of-the-art optimization algorithms such as FISTA and ADMM. Finally, we will describe the most recent work that uses learned-priors for improving the quality of image reconstruction under multiple scattering.

    Keywords : diffractive imaging; Fourier ptychography; model-based imaging; multiple scattering; plug-and-play prior; image reconstruction; inverse problems

    Codes MSC :
    62H35 - Image analysis (statistics)
    65K10 - Optimization and variational techniques
    65N21 - Inverse problems
    65T50 - Discrete and fast Fourier transforms (numerical methods)
    94A08 - Image processing (compression, reconstruction, etc.)
    94A12 - Signal theory (characterization, reconstruction, filtering, etc.)

    Ressources complémentaires :

      Informations sur la Vidéo

      Langue : Anglais
      Date de publication : 28/11/2018
      Date de captation : 22/11/2018
      Collection : Research schools ; Numerical Analysis and Scientific Computing ; Probability and Statistics
      Format : MP4
      Durée : 01:04:06
      Domaine : Numerical Analysis & Scientific Computing ; Probability & Statistics
      Audience : Chercheurs ; Doctorants , Post - Doctorants ; Etudiants Science Cycle 2
      Download : https://videos.cirm-math.fr/2018-11-22_Kamilov.mp4

    Informations sur la rencontre

    Nom de la rencontre : International traveling workshop on interactions between low-complexity data models and sensing techniques / Colloque international et itinérant sur les interactions entre modèles de faible complexité et acquis
    Organisateurs de la rencontre : Anthoine, Sandrine ; Boursier, Yannick ; Jacques, Laurent
    Dates : 19/11/2018 - 23/11/2018
    Année de la rencontre : 2018
    URL Congrès : https://conferences.cirm-math.fr/1865.html

    Citation Data

    DOI : 10.24350/CIRM.V.19477403
    Cite this video as: Kamilov, Ulugbek (2018). Signal processing for nonlinear diffractive imaging. CIRM. Audiovisual resource. doi:10.24350/CIRM.V.19477403
    URI : http://dx.doi.org/10.24350/CIRM.V.19477403

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