119 research outputs found
Texture Synthesis Through Convolutional Neural Networks and Spectrum Constraints
This paper presents a significant improvement for the synthesis of texture
images using convolutional neural networks (CNNs), making use of constraints on
the Fourier spectrum of the results. More precisely, the texture synthesis is
regarded as a constrained optimization problem, with constraints conditioning
both the Fourier spectrum and statistical features learned by CNNs. In contrast
with existing methods, the presented method inherits from previous CNN
approaches the ability to depict local structures and fine scale details, and
at the same time yields coherent large scale structures, even in the case of
quasi-periodic images. This is done at no extra computational cost. Synthesis
experiments on various images show a clear improvement compared to a recent
state-of-the art method relying on CNN constraints only
Comparaison de la composition de deux images, et application Ă la recherche automatique
Nous présentons une méthode nouvelle permettant de tester la proximité de deux images, en rejetant une hypothèse d'indépendance. Cette méthode repose sur une représentation géométrique de l'image: ses ensembles de niveaux sont vus comme des fermés aléatoires et sont comparés grâce à des résultats élémentaires de géométrie stochastique. Le critère de ressemblance résultant compare les grandes lignes de l'organisation globale des images. Enfin, nous appliquons ce test à la recherche automatique dans une base d'images à partir d'un exemple, qui peut être une image ou un schéma de composition. Nous indiquons également comment décider si la mise en correspondance de deux images est pertinante, en fixant les seuils de comparaison grâce à des réalisations d'un modèle aléatoire d'image, le modèle feuilles mortes
A Bayesian Hyperprior Approach for Joint Image Denoising and Interpolation, with an Application to HDR Imaging
Recently, impressive denoising results have been achieved by Bayesian
approaches which assume Gaussian models for the image patches. This improvement
in performance can be attributed to the use of per-patch models. Unfortunately
such an approach is particularly unstable for most inverse problems beyond
denoising. In this work, we propose the use of a hyperprior to model image
patches, in order to stabilize the estimation procedure. There are two main
advantages to the proposed restoration scheme: Firstly it is adapted to
diagonal degradation matrices, and in particular to missing data problems (e.g.
inpainting of missing pixels or zooming). Secondly it can deal with signal
dependent noise models, particularly suited to digital cameras. As such, the
scheme is especially adapted to computational photography. In order to
illustrate this point, we provide an application to high dynamic range imaging
from a single image taken with a modified sensor, which shows the effectiveness
of the proposed scheme.Comment: Some figures are reduced to comply with arxiv's size constraints.
Full size images are available as HAL technical report hal-01107519v5, IEEE
Transactions on Computational Imaging, 201
A geometrically aware auto-encoder for multi-texture synthesis
We propose an auto-encoder architecture for multi-texture synthesis. The
approach relies on both a compact encoder accounting for second order neural
statistics and a generator incorporating adaptive periodic content. Images are
embedded in a compact and geometrically consistent latent space, where the
texture representation and its spatial organisation are disentangled. Texture
synthesis and interpolation tasks can be performed directly from these latent
codes. Our experiments demonstrate that our model outperforms state-of-the-art
feed-forward methods in terms of visual quality and various texture related
metrics.Comment: Error in table 1 correcte
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