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Hardware implementation of real-time multiple frame super-resolution

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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

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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

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

Sabine Süsstrunk, Tong Zhang, Dong Liu, Ruofan Zhou, Fayez Lahoud, Radu Timofte, Chang Chen, Soumik Sarkar

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