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A new objective metric to predict image quality using deep neural networks

Publications associées (40)

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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 ...

Springer2018

Learning to See through Reflections

Sabine Süsstrunk

"Pictures of objects behind a glass are difficult to interpret" "and understand due to the superposition of two real images: a reflection layer and a background layer. Separation of these two layers is challenging due to the ambiguities in as- signing text ...

IEEE2018

Color Representation in Deep Neural Networks

Sabine Süsstrunk, Martin Pierre Engilberge, Edo Collins

Convolutional neural networks are top-performers on image classification tasks. Understanding how they make use of color information in images may be useful for various tasks. In this paper we analyze the representation learned by a popular CNN to detect a ...

2017

Learning the weight matrix for sparsity averaging in compressive imaging

Jean-Philippe Thiran, Dimitris Perdios, Adrien Georges Jean Besson

We propose to map the fast iterative shrinkage-thresholding algorithm to a deep neural network (DNN), with a sparsity prior in a concatenation of wavelet bases, in the context of compressive imaging. We exploit the DNN architecture to learn the optimal wei ...

2017

Perceptual Losses for Real-Time Style Transfer and Super-Resolution

Alexandre Massoud Alahi

We consider image transformation problems, where an input image is transformed into an output image. Recent methods for such problems typically train feed-forward convolutional neural networks using a per-pixel loss between the output and ground-truth imag ...

Springer2016

Practical Approaches for Software Components Integration in Telecommunications

Sébastien Rumley, Christian Gaumier

Nowadays, advances in telecommunication network design and performance analysis often rely on dedicated software tools. Unfortunately, developing new tools is a very time and resources consuming activity. To rationalise development costs, existing applicat ...

2008

Artificial neural networks in spectrum fatigue life prediction of composite materials

Anastasios Vassilopoulos

An artificial neural network has been proved to be a sufficient tool for modelling fatigue life of multidirectional composite laminates made of GFRP composite materials and tested under constant amplitude loading patterns. Modelling efficiency of the netwo ...

Elsevier2007

Adaptive Weight Change Mechanism for Kohonens's Neural Network Implemented in CMOS 0.18 μm Technology

Rafal Tomasz Dlugosz

In this paper, we present a block of adaptive weight change (AWC) mechanism for analog current-mode Kohonen's Neural Network (KNN) implemented in CMOS 0.18 μm technology. As some other essential building blocks of KNNs dealing with the calculations of the ...

2007

Optimization of high order perceptrons

Neural networks are widely applied in research and industry. However, their broader application is hampered by various technical details. Among these details are several training parameters and the choice of the topology of the network. The subject of this ...

École Polytechnique Fédérale de Lausanne1997

Optimization of high order perceptrons

EPFL1997