Concept

Coherent diffraction imaging

Related publications (53)

Phase Retrieval: From Computational Imaging to Machine Learning: A tutorial

Michaël Unser, Thanh-An Michel Pham, Jonathan Yuelin Dong

Phase retrieval consists in the recovery of a complex-valued signal from intensity-only measurements. As it pervades a broad variety of applications, many researchers have striven to develop phase-retrieval algorithms. Classical approaches involve techniqu ...

2023

Learning-based techniques for lensless reconstruction

Yohann Loïc Yann Perron

In this internship, I explore different optimization algorithms for lensless imaging. Lensless imaging is a new imaging technique that replaces the lens of a camera with a diffuser mask. This allows for simpler and cheaper camera hardware. However, the rec ...

2023

Une version polyatomique de l'algorithme Frank-Wolfe pour résoudre le problème LASSO en grandes dimensions

Julien René Pierre Fageot, Adrian Thibault Etienne Jarret, Matthieu Martin Jean-André Simeoni

Nous nous intéressons à la reconstruction parcimonieuse d’images à l’aide du problème d’optimisation régularisé LASSO. Dans de nombreuses applications pratiques, les grandes dimensions des objets à reconstruire limitent, voire empêchent, l’utilisation des ...

2022

Phase Reconstruction of Low-Energy Electron Holograms of Individual Proteins

Klaus Kern, Stephan Rauschenbach, Sven Alexander Szilagyi, Hannah Julia Ochner

Low-energy electron holography (LEEH) is one of the few techniques capable of imaging large and complex three-dimensional molecules, such as proteins, on the single molecule level at subnanometer resolution. During the imaging process, the structural infor ...

AMER CHEMICAL SOC2022

Stochasticity helps to navigate rough landscapes: comparing gradient-descent-based algorithms in the phase retrieval problem

Lenka Zdeborová, Francesca Mignacco

In this paper we investigate how gradient-based algorithms such as gradient descent (GD), (multi-pass) stochastic GD, its persistent variant, and the Langevin algorithm navigate non-convex loss-landscapes and which of them is able to reach the best general ...

IOP PUBLISHING LTD2021

Refinement for single-nanoparticle structure determination from low-quality single-shot coherent diffraction data

Christoph Bostedt, Christophe Nicolas

With the emergence of X-ray free-electron lasers, it is possible to investigate the structure of nanoscale samples by employing coherent diffractive imaging in the X-ray spectral regime. In this work, we developed a refinement method for structure reconstr ...

INT UNION CRYSTALLOGRAPHY2020

3D nanometrology of transparent objects by phase calibration of a basic bright-field microscope for multiple illumination apertures

Martinus Gijs, Daniel Migliozzi, Bingying Zhao

Optical retrieval of the structure of transparent objects at the nanoscale requires adapted techniques capable of probing their interaction with light. Here, we considered a method based on calibration of the defocusing with partially coherent illumination ...

2020

Monitoring Spontaneous Charge-Density Fluctuations by Single-Molecule Diffraction of Quantum Light

Homodyne X-ray diffraction signals produced by classical light and classical detectors are given by the modulus square of the charge density in momentum space vertical bar sigma(q)vertical bar(2), missing its phase, which is required in order to invert the ...

2019

Color neutral nanocomposite nickel-tantalum oxide for electrochromic windows

Josef Andreas Schuler, Olivia Valérie Charlotte Bouvard

Electrochromic windows can darken on demand to limit the solar gains entering a building to reduce the risk of overheating, while preserving the view towards the exterior. Yet, the switching speed and contrast as well as the durability of commercial produc ...

IOP2019

Deep Feature Factorization For Content-Based Image Retrieval And Localization

Sabine Süsstrunk, Edo Collins

State of the art content-based image retrieval algorithms owe their excellent performance to the rich semantics encoded in the deep activations of a convolutional neural network. The difference between these algorithms lies mostly in how activations are co ...

IEEE2019

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