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 à 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