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Hardware implementation of real-time multiple frame super-resolution

Publications associées (39)

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Cross-resolution Face Recognition via Identity-Preserving Network and Knowledge Distillation

Touradj Ebrahimi, Yuhang Lu

Cross-resolution face recognition has become a challenging problem for modern deep face recognition systems. It aims at matching a low-resolution probe image with high-resolution gallery images registered in a database. Existing methods mainly leverage pri ...

2023

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

Large-Format Time-Gated SPAD Cameras for Real-Time Phasor-Based FLIM

Arin Can Ülkü

Fluorescence lifetime imaging microscopy (FLIM) is an imaging modality often used to monitor biochemical properties of a cell or a tissue. In addition to conventional fluorescence microscopy features, such as selective labeling and non-invasiveness, FLIM e ...

EPFL2021

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

Jean-Philippe Thiran, Thomas Yu, Seyedbehzad Bozorgtabar, Claudiu-Cristian Musat, Hazim Kemal Ekenel, Mohammad Saeed Rad

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 ...

2021

Towards Real-World Super-Resolution using Deep Neural Networks

Ruofan Zhou

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

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

Multi-modal Spectral Image Super-Resolution

Sabine Süsstrunk, Ruofan Zhou, Fayez Lahoud

Recent advances have shown the great power of deep convolutional neural networks (CNN) to learn the relationship between low and high-resolution image patches. However, these methods only take a single-scale image as input and require large amount of data ...

SPRINGER INTERNATIONAL PUBLISHING AG2019

Deep convolutional neural networks (CNNs), trained on corresponding pairs of high- and low-resolution images, achieve state-of-the-art performance in single-image super- resolution and surpass previous signal-processing based approaches. However, their per ...

IEEE COMPUTER SOC2019

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In this paper, we describe the Perceptual Image Restoration and Manipulation (PIRM) workshop challenge on spectral image super-resolution, motivate its structure and conclude on results obtained by the participants. The challenge is one of the first of its ...

SPRINGER INTERNATIONAL PUBLISHING AG2019

Background: Structured illumination microscopy (SIM) is a family of methods in optical fluorescence microscopy that can achieve both optical sectioning and super-resolution effects. SIM is a valuable method for high-resolution imaging of fixed cells or tis ...

Oxford University Press (OUP)2019