Publications associées à Déconvolution

Correlating cryo-super resolution radial fluctuations and dual-axis cryo-scanning transmission electron tomography to bridge the light-electron resolution gap

Prabhu Prasad Swain

Visualization of organelles and their interactions with other features in the native cell remains a challenge in modern biology. We have introduced cryo-scanning transmission electron tomography (CSTET), which can ac-cess 3D volumes on the scale of 1 micro ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2023

Adaptive optics enables multimode 3D super-resolution microscopy via remote focusing

Aleksandra Radenovic, Vytautas Navikas, Sanjin Marion, Adrien Charles-François Raymond Descloux, Kristin Stefanie Grussmayer

A variety of modern super-resolution micro-scopy methods provide researchers with previouslyinconceivable biological sample imaging opportunities ata molecular resolution. All of these techniques excel atimaging samples that are close to the coverslip ...

2021

DiffuserCam Project

William Cappelletti, Jonathan Philippe Reymond, Matteo Pariset

We test the reconstruction power of a lensless cam- era on a custom dataset. We define a linear inverse problem on the raw image, based on a PSF estimation of the the sensing device and the actual captures. We implement various regularizations to enforce s ...

2021

Spatially-Variant CNN-Based Point Spread Function Estimation for Blind Deconvolution and Depth Estimation in Optical Microscopy

Michael Stefan Daniel Liebling, Adrian Shajkofci

Optical microscopy is an essential tool in biology and medicine. Imaging thin, yet non-flat objects in a single shot (without relying on more sophisticated sectioning setups) remains challenging as the shallow depth of field that comes with high-resolution ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Optical characterization of high numerical aperture microlenses for quality assessment and fabrication process optimization

Toralf Scharf, Wilfried Noell, Jeremy Béguelin

Accurate characterization of high numerical aperture aspheric microlenses currently is a nonstandard procedure that remains an open challenge. Here, we present and discuss a characterization method based on interferometric and point spread function measure ...

OPTICAL SOC AMER2020

Graph Spectral Clustering of Convolution Artefacts in Radio Interferometric Images

Paul Hurley, Matthieu Martin Jean-André Simeoni

The starting point for deconvolution methods in radioastronomy is an estimate of the sky intensity called a dirty image. These methods rely on the telescope point-spread function so as to remove artefacts which pollute it. In this work, we show that the in ...

IEEE2019

Semi-blind spatially-variant deconvolution in optical microscopy with local Point Spread Function estimation by use of Convolutional Neural Networks

Michael Stefan Daniel Liebling, Adrian Shajkofci

We present a semi-blind, spatially-variant deconvolution technique aimed at optical microscopy that combines a local estimation step of the point spread function (PSF) and deconvolution using a spatially variant, regularized Richardson-Lucy algorithm. To f ...

Idiap2018

Semi-Blind Spatially-Variant Deconvolution in Optical Microscopy with Local Point Spread Function Estimation By Use Of Convolutional Neural Networks

Michael Stefan Daniel Liebling, Adrian Shajkofci

2018

DeconvolutionLab2: An Open-Source Software for Deconvolution Microscopy

Michaël Unser, Daniel Sage, Laurène Donati, Cédric René Jean Vonesch, Ferréol Arnaud Marie Soulez, Denis Fortun, Guillaume Schmit

Images in fluorescence microscopy are inherently blurred due to the limit of diffraction of light. The purpose of deconvolution microscopy is to compensate numerically for this degradation. Deconvolution is widely used to restore fine details of 3D biologi ...

2017

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Déconvolution

Correlating cryo-super resolution radial fluctuations and dual-axis cryo-scanning transmission electron tomography to bridge the light-electron resolution gap

Finite-resolution Deconvolution of Multiwavelength Imaging of 20,000 Galaxies in the COSMOS Field: The Evolution of Clumpy Galaxies over Cosmic Time

Adaptive optics enables multimode 3D super-resolution microscopy via remote focusing

DiffuserCam Project

Spatially-Variant CNN-Based Point Spread Function Estimation for Blind Deconvolution and Depth Estimation in Optical Microscopy

Optical characterization of high numerical aperture microlenses for quality assessment and fabrication process optimization

Graph Spectral Clustering of Convolution Artefacts in Radio Interferometric Images

Semi-blind spatially-variant deconvolution in optical microscopy with local Point Spread Function estimation by use of Convolutional Neural Networks

Semi-Blind Spatially-Variant Deconvolution in Optical Microscopy with Local Point Spread Function Estimation By Use Of Convolutional Neural Networks

DeconvolutionLab2: An Open-Source Software for Deconvolution Microscopy

Graph Chatbot

Chattez avec Graph Search

Finite-resolution Deconvolution of Multiwavelength Imaging of 20,000 Galaxies in the COSMOS Field: The Evolution of Clumpy Galaxies over Cosmic Time

Adaptive optics enables multimode 3D super-resolution microscopy via remote focusing

Spatially-Variant CNN-Based Point Spread Function Estimation for Blind Deconvolution and Depth Estimation in Optical Microscopy

Correlating cryo-super resolution radial fluctuations and dual-axis cryo-scanning transmission electron tomography to bridge the light-electron resolution gap

DiffuserCam Project

DeconvolutionLab2: An Open-Source Software for Deconvolution Microscopy

Optical characterization of high numerical aperture microlenses for quality assessment and fabrication process optimization

Graph Spectral Clustering of Convolution Artefacts in Radio Interferometric Images

Semi-blind spatially-variant deconvolution in optical microscopy with local Point Spread Function estimation by use of Convolutional Neural Networks

Semi-Blind Spatially-Variant Deconvolution in Optical Microscopy with Local Point Spread Function Estimation By Use Of Convolutional Neural Networks