Publications associées à Restauration (image)

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

Evaluating measurement uncertainty in Brillouin distributed optical fibre sensors using image denoising

Luc Thévenaz, Marcelo Alfonso Soto Hernandez, Zhisheng Yang, Simon Adrien Zaslawski

In 2016, our research team proposed in an issue of Nature Communications1 the use of multidimensional signal processing, especially image denoising techniques, to improve the signal-to-noise ratio (SNR) of distributed optical ﬁbre sensors. The beneﬁts of t ...

2021

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

Meritxell Bach Cuadra, Tobias Kober, Tom Hilbert, Thomas Yu, Sébastien Tourbier, Reto Meuli, Patric Hagmann, Hamza Kebiri

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 ...

SPRINGER INTERNATIONAL PUBLISHING AG2021

Towards image denoising in the latent space of learning-based compression

Touradj Ebrahimi, Evgeniy Upenik, Michela Testolina

In recent years, learning-based image compression has demonstrated similar or superior performance when com- pared to conventional approaches in terms of compression efficiency and visual quality. Typically, learning-based image compression takes advantage ...

SPIE2021

Deep Gaussian denoiser epistemic uncertainty and decoupled dual-attention fusion

Sabine Süsstrunk, Majed El Helou, Xiaoyu Lin, Xiaoqi Ma

Following the performance breakthrough of denoising networks, improvements have come chiefly through novel architecture designs and increased depth. While novel denoising networks were designed for real images coming from different distributions, or for sp ...

2021

Image Restoration using Plug-and-Play CNN MAP Denoisers

Siavash Arjomand Bigdeli, David Honzátko

Plug-and-play denoisers can be used to perform generic image restoration tasks independent of the degradation type. These methods build on the fact that the Maximum a Posteriori (MAP) optimization can be solved using smaller sub-problems, including a MAP d ...

SCITEPRESS2020

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

On the 2D Post-Processing of Brillouin Optical Time-Domain Analysis

Luc Thévenaz, Zhisheng Yang, Simon Adrien Zaslawski

The benefits and limitations inherent to the 2D post-processing of measurements from Brillouin optical time-domain analyzers are investigated from a fundamental point of view. In a preliminary step, the impact of curve fitting on the precision of the estim ...

2020

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Restauration (image)

Annotation-efficient image anomaly detection

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

Evaluating measurement uncertainty in Brillouin distributed optical fibre sensors using image denoising

Deep Image Restoration: Between Data Fidelity and Learned Priors

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

Towards image denoising in the latent space of learning-based compression

Deep Gaussian denoiser epistemic uncertainty and decoupled dual-attention fusion

Image Restoration using Plug-and-Play CNN MAP Denoisers

Blind Universal Bayesian Image Denoising With Gaussian Noise Level Learning

On the 2D Post-Processing of Brillouin Optical Time-Domain Analysis

Graph Chatbot

Chattez avec Graph Search

Annotation-efficient image anomaly detection

Towards image denoising in the latent space of learning-based compression

Image Restoration using Plug-and-Play CNN MAP Denoisers

Blind Universal Bayesian Image Denoising With Gaussian Noise Level Learning

Evaluating measurement uncertainty in Brillouin distributed optical fibre sensors using image denoising

Deep Image Restoration: Between Data Fidelity and Learned Priors

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

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

Deep Gaussian denoiser epistemic uncertainty and decoupled dual-attention fusion

On the 2D Post-Processing of Brillouin Optical Time-Domain Analysis