69 citations · 249 across the 23 of their papers we have counts for
5 papers · 1 filter
A Simple Framework to Leverage State-Of-The-Art Single-Image Super-Resolution Methods to Restore Light Fields
Reuben A. Farrugia, C. Guillemot
Plenoptic cameras offer a cost effective solution to capture light fields by multiplexing multiple views on a single image sensor. However, the high angular resolution is achieved…
Context-adaptive neural network based prediction for image compression
Thierry Dumas, Aline Roumy, Christine Guillemot
This paper describes a set of neural network architectures, called Prediction Neural Networks Set (PNNS), based on both fully-connected and convolutional neural networks, for intra…
Learning Discriminative Multilevel Structured Dictionaries for Supervised Image Classification
Jeremy Aghaei Mazaheri, Elif Vural, Claude Labit +1
Sparse representations using overcomplete dictionaries have proved to be a powerful tool in many signal processing applications such as denoising, super-resolution, inpainting, com…
Autoencoder based image compression: can the learning be quantization independent?
Thierry Dumas, Aline Roumy, Christine Guillemot
This paper explores the problem of learning transforms for image compression via autoencoders. Usually, the rate-distortion performances of image compression are tuned by varying t…
Light Field Super-Resolution using a Low-Rank Prior and Deep Convolutional Neural Networks
Reuben A. Farrugia, Christine Guillemot
Light field imaging has recently known a regain of interest due to the availability of practical light field capturing systems that offer a wide range of applications in the field…