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PIRM2018 Challenge on Spectral Image Super-Resolution: Methods and Results

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Pixel super-resolution with spatially entangled photons

Edoardo Charbon, Jiuxuan Zhao

Pixelation is common in quantum imaging systems and limit the image spatial resolution. Here, the authors introduce a pixel super-resolution approach based on measuring the full spatially-resolved joint probability distribution of spatially-entangled photo ...

NATURE PORTFOLIO2022

Spectral imaging is a fundamental diagnostic technique with widespread application. Conventional spectral imaging approaches have intrinsic limitations on spatial and spectral resolutions due to the physical components they rely on. To overcome these physi ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Context-Aware Image Super-Resolution Using Deep Neural Networks

Mohammad Saeed Rad

Image super-resolution is a classic ill-posed computer vision and image processing problem, addressing the question of how to reconstruct a high-resolution image from its low-resolution counterpart. Current state-of-the-art methods have improved the perfor ...

EPFL2021

Direct time-of-flight SPAD image sensors for light detection and ranging

Preethi Padmanabhan

Depth sensing is an increasingly important feature in many applications of consumer, automotive, augmented/virtual reality (AR-VR), space and bio-medical imaging. Long range, high depth resolution, high spatial resolution, and high frame rates are often co ...

EPFL2021

Image super-resolution reconstructs a higher-resolution image from the observed low-resolution image. In recent years, machine learning models have been widely employed and deep learning networks have achieved state-of-the-art super-resolution performance. ...

EPFL2020

Homogeneous multifocal excitation for high-throughput super-resolution imaging

Pierre Gönczy, Suliana Manley, Kyle Michael Douglass, Khalid Ashraf Mohie Ibrahim, Davide Gambarotto, Paul Guichard, Virginie Hamel, Denis Fortun, Niccolo Banterle, Dora Mahecic

Super-resolution microscopies have become an established tool in biological research. However, imaging throughput remains a main bottleneck in acquiring large datasets required for quantitative biology. Here we describe multifocal flat illumination for fie ...

NATURE PUBLISHING GROUP2020

Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields With a Generative Adversarial Network

Alexis Berne, Jussi Sakari Leinonen, Daniele Nerini

Generative adversarial networks (GANs) have been recently adopted for super-resolution, an application closely related to what is referred to as "downscaling'' in the atmospheric sciences: improving the spatial resolution of low-resolution images. The abil ...

2020

Benefiting from Multitask Learning to Improve Single Image Super-Resolution

Jean-Philippe Thiran, Claudiu-Cristian Musat, Hazim Kemal Ekenel, Mohammad Saeed Rad

Despite significant progress toward super resolving more realistic images by deeper convolutional neural networks (CNNs), reconstructing fine and natural textures still remains a challenging problem. Recent works on single image super resolution (SISR) are ...

2020

Light microscopy is a tool of paramount importance for biologists and has been constantly improved for the past four centuries. Despite many recent developments, microscopy techniques still require improvement, especially to reach better temporal and spect ...

EPFL2020

Subcellular Chemical Imaging: New Avenues in Cell Biology

Melissa Kathleen Passarelli

To better understand the physiology and acclimation capability of the cell, one of the great challenges of the future is to access the interior of a cell and unveil its chemical landscape (composition and distribution of elements and molecules). Chemical i ...

ELSEVIER SCIENCE LONDON2020