Search Results for author:
Found 6 papers, 3 papers with code
A Sliced Wasserstein Loss for Neural Texture Synthesis
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 recognition (e. g. VGG-19).
Unsupervised Deep Single-Image Intrinsic Decomposition using Illumination-Varying Image Sequences
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 known and realistic ground truth decompositions.
WESPE: Weakly Supervised Photo Enhancer for Digital Cameras
Low-end and compact mobile cameras demonstrate limited photo quality mainly due to space, hardware and budget constraints.
DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks
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 to achieve the quality results of DSLR cameras.
Repeated Pattern Detection using CNN activations
We propose a new approach for detecting repeated patterns on a grid in a single image.
DARN: a Deep Adversial Residual Network for Intrinsic Image Decomposition
We present a new deep supervised learning method for intrinsic decomposition of a single image into its albedo and shading components.
