2 citations · 2 across the 2 of their papers we have counts for
5 papers
A Sliced Wasserstein Loss for Neural Texture Synthesis
Eric Heitz, Kenneth Vanhoey, Thomas Chambon +1
We address the problem of computing a textural loss based on the statistics extracted from the feature activations of a convolutional neural network optimized for object recognitio…
Unsupervised Deep Single-Image Intrinsic Decomposition using Illumination-Varying Image Sequences
Louis Lettry, Kenneth Vanhoey, Luc van Gool
Machine learning based Single Image Intrinsic Decomposition (SIID) methods decompose a captured scene into its albedo and shading images by using the knowledge of a large set of kn…
WESPE: Weakly Supervised Photo Enhancer for Digital Cameras
Andrey Ignatov, Nikolay Kobyshev, Radu Timofte +2
Low-end and compact mobile cameras demonstrate limited photo quality mainly due to space, hardware and budget constraints. In this work, we propose a deep learning solution that tr…
DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks
Andrey Ignatov, Nikolay Kobyshev, Radu Timofte +2
Despite a rapid rise in the quality of built-in smartphone cameras, their physical limitations - small sensor size, compact lenses and the lack of specific hardware, - impede them…
DARN: a Deep Adversial Residual Network for Intrinsic Image Decomposition
Louis Lettry, Kenneth Vanhoey, Luc Van Gool
We present a new deep supervised learning method for intrinsic decomposition of a single image into its albedo and shading components. Our contributions are based on a new fully co…