1 citations · 2 across the 4 of their papers we have counts for
7 papers
Analysis of Different Losses for Deep Learning Image Colorization
Coloma Ballester, Aurélie Bugeau, Hernan Carrillo +4
Image colorization aims to add color information to a grayscale image in a realistic way. Recent methods mostly rely on deep learning strategies. While learning to automatically co…
An Analysis of Generative Methods for Multiple Image Inpainting
Coloma Ballester, Aurelie Bugeau, Samuel Hurault +2
Image inpainting refers to the restoration of an image with missing regions in a way that is not detectable by the observer. The inpainting regions can be of any size and shape. Th…
Influence of Color Spaces for Deep Learning Image Colorization
Coloma Ballester, Aurélie Bugeau, Hernan Carrillo +4
Colorization is a process that converts a grayscale image into a color one that looks as natural as possible. Over the years this task has received a lot of attention. Existing col…
Automatic Flare Spot Artifact Detection and Removal in Photographs
Patricia Vitoria, Coloma Ballester
Flare spot is one type of flare artifact caused by a number of conditions, frequently provoked by one or more high-luminance sources within or close to the camera field of view. Wh…
Non-uniform Blur Kernel Estimation via Adaptive Basis Decomposition
Guillermo Carbajal, Patricia Vitoria, Mauricio Delbracio +2
Motion blur estimation remains an important task for scene analysis and image restoration. In recent years, the removal of motion blur in photographs has seen impressive progress i…
ChromaGAN: Adversarial Picture Colorization with Semantic Class Distribution
Patricia Vitoria, Lara Raad, Coloma Ballester
The colorization of grayscale images is an ill-posed problem, with multiple correct solutions. In this paper, we propose an adversarial learning colorization approach coupled with…