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A Bit Too Much? High Speed Imaging from Sparse Photon Counts

Related publications (49)

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Data for: "A Neural-Network-Based Convex Regularizer for Inverse Problems"

Michaël Unser, Pakshal Narendra Bohra, Alexis Marie Frederic Goujon, Sebastian Jonas Neumayer, Stanislas Ducotterd

Data for: "A Neural-Network-Based Convex Regularizer for Inverse Problems". The corresponding scripts can be accessed on GitHub (https://github.com/axgoujon/convex_ridge_regularizers). The data is organized as follows: - ct_data_sets.tar.gz: contains prepr ...

EPFL Infoscience2023

Plasma-based Electroacoustic Actuator for Broadband Sound Absorption

Stanislav Sergeev

Environmental noise, mostly related to human activities, has an immense impact on public health. The development of noise reduction technologies is paramount in addressing this problem. Because of practical and economic reasons, a compact, broadband, light ...

EPFL2022

An Ultra Low-Noise Micropower PPG Sensor

Antonino Caizzone

Nowadays, efficient and remote health monitoring is becoming increasingly important, given both the ageing of population and the combined action of an increase in obesity level and cardiovascular diseases. The healthcare industry is becoming more reliant o ...

EPFL2020

Blind Universal Bayesian Image Denoising With Gaussian Noise Level Learning

Sabine Süsstrunk, Majed El Helou

Blind and universal image denoising consists of using a unique model that denoises images with any level of noise. It is especially practical as noise levels do not need to be known when the model is developed or at test time. We propose a theoretically-gr ...

IEEE2020

Modeling for Ultra Low Noise CMOS Image Sensors

Raffaele Capoccia

The low-light performance of a CMOS image sensor (CIS) is one of the most important performance metrics in a camera, whether it is used in products for consumer electronics or in an image-acquisition system for machine vision or the Internet-of-Things (IoT ...

EPFL2020

Blind Universal Bayesian Image Denoising with Gaussian Noise Level Learning

Sabine Süsstrunk, Majed El Helou

Blind and universal image denoising consists of a unique model that denoises images with any level of noise. It is especially practical as noise levels do not need to be known when the model is developed or at test time. We propose a theoretically-grounded ...

2019

We study the effect of the stochastic gradient noise on the training of generative adversarial networks (GANs) and show that it can prevent the convergence of standard game optimization methods, while the batch version converges. We address this issue with ...

2019

Various unsupervised learning algorithms including GMM, Birch, Mean- Shift, K-means and DBSCAN were used to cluster the image and depth sensor data. However, it was not possible to ﬁt the clusters on each objects quite separately in as much as data are too ...

2019

Objective evaluation of static beamforming on the quality of speech in noise

Hervé Lissek, Gilles André Courtois, Vincent Pierre Olivier Grimaldi

Beamforming is commonly used in devices such as in hearing instruments (HI) with a view to reducing noise. It consists in focusing on audio signals arising from a prescribed steering direction while suppressing those coming from other directions. The effic ...

European Acoustics Association2018

We consider optimal information acquisition for the control of linear discrete-time random systems with noisy observations and apply the findings to the problem of dynamically implementing emissions-reduction targets. The optimal policy, which is provided ...

2018