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Attribute-based representations help machine learning models perform tasks based on human understandable concepts, allowing a closer human-machine collaboration. However, learning attributes that accurately reflect the content of an image is not always str ...

Medipix4 is the latest member in the Medipix/Timepix family of pixel detector chips aimed at high rate spectroscopic X-ray imaging using high-Z materials. The chip address the limitations of conventional hybrid pixel detectors for X-ray imaging. Its predec ...

Image restoration reconstructs, as faithfully as possible, an original image from a potentially degraded version of it. Image degradations can be of various types, for instance haze, unwanted reflections, optical or spectral aberrations, or other physicall ...

Event-driven acquisition uses neural-network-based recognition of specific biological events to trigger switching between slow and fast super-resolution imaging, enriching the capture of interesting events with high spatiotemporal resolution. A common goal ...

Accurate characterization of in utero human brain maturation is critical as it involves complex interconnected structural and functional processes that may influence health later in life. Magnetic resonance imaging is a powerful tool complementary to the u ...

This paper describes the development of a novel medical x-ray imaging system adapted to the needs and constraints of low- and middle-income countries. The developed system is based on an indirect conversion chain: a scintillator plate produces visible ligh ...

Localization microscopy is a super-resolution imaging technique that relies on the spatial and temporal separation of blinking fluorescent emitters. These blinking events can be individually localized with a precision significantly smaller than the classic ...

Among the medical imaging modalities, ultrasound (US) imaging is one of the safest, most widespread, and least expensive method used in medical diagnosis. In the past decades, several technological advances enabled the advent of ultrafast US imaging, an ac ...

Explainable machine learning and uncertainty quantification have emerged as promising approaches to check the suitability and understand the decision process of a data-driven model, to learn new insights from data, but also to get more information about th ...

Single-molecule localization microscopy (SMLM) describes a family of powerful imaging techniques that dramatically improve spatial resolution over standard, diffraction-limited microscopy techniques and can image biological structures at the molecular scal ...

Super-resolution (SR) has traditionally been based on pairs of high-resolution images (HR) and their low-resolution (LR) counterparts obtained artificially with bicubic downsampling. However, in real-world SR, there is a large variety of realistic image de ...

Enhancing local-Transmitting less-Improving global

Chattez avec Graph Search

Attribute Prediction as Multiple Instance Learning

Medipix4, a high granularity four sides buttable pixel readout chip for high resolution spectroscopic X-ray imaging at rates compatible with medical CT scans

Deep Image Restoration: Between Data Fidelity and Learned Priors

Event-driven acquisition for content-enriched microscopy

Simulated Half-Fourier Acquisitions Single-shot Turbo Spin Echo (HASTE) of the Fetal Brain: Application to Super-Resolution Reconstruction

X-ray imaging detector for radiological applications adapted to the context and requirements of low- and middle-income countries

Parameter-free rendering of single-molecule localization microscopy data for parameter-free resolution estimation

Ultrasound Imaging: From Physical Modeling to Deep Learning

A Whale's Tail - Finding the Right Whale in an Uncertain World

Single-molecule localization microscopy

Benefiting From Bicubically Down-Sampled Images for Learning Real-World Image Super-Resolution