[PDF] Top 20 Wasserstein Loss for Image Synthesis and Restoration

... Variational Restoration. While it is natural to think of texture synthesis as a statistical re-sampling problem rather than an optimization one, a large body of literature on image restoration ... Voir le document complet

... texture synthesis, which is achieved by simply applying a memory-limited quasi-Newton (L-BFGS) optimization scheme to this statistical ...(wavelet-based) and non-linear (sparse coding) features illustrate ... Voir le document complet

... in image and video processing ...gradient-descent and back-propagation algorithms which requires to calculate the gradient of the loss ...differentiable, and despite their superior ... Voir le document complet

... of using proximal MCMC approaches [27, 28] to tackle the non- differentiability of the potential function associated to π thanks to Moreau-Yoshida regularization [31]. However, these approaches re- quire the existence of ... Voir le document complet

... natural image testsets and on the dead leaves testset, we observe that the model trained on dead leaves outperforms by a large margin all other models trained on alternative synthetic image datasets ... Voir le document complet

... oscillations and thus limit the apparition of arti- facts when applied to images that differ from the distribution of images considered during training ...[5]. Loss terms that enforce a good fit with the ... Voir le document complet

... thesis and to give better results when compared to simple template ...methods for calculating the linear weighting ...tion for approximating the template. The proposed texture synthesis ... Voir le document complet

... layers and loss functions, and can be trained using standard backpropagation algorithms based on stochastic gradient descent or its mod- ifications ...created for almost any existing ... Voir le document complet

... area and σ x 2 its ...rejection and, consequently, an improved ability to tell apart different gray ...values for the proposed and reference algorithms (we discard PPB and BM3D in the ... Voir le document complet

... framework for the learning of a compact convolutional neural network modeling the local patch regularity ...of Wasserstein generative adversarial models ...efficient for image denoising ... Voir le document complet

... work and motivations The extension of TV-based models to multicomponent images is, in general, non ...channel-by-channel and then summing up the resulting smoothness measures [12, 13, 14, 15, ...smearing ... Voir le document complet

... DDID and their re-fitted versions as a function of the smoothing parameter ...Monarch image and DDID on the Lena ...performance for a larger smoothing ...the loss in terms of ...lost, ... Voir le document complet

... of superdifferentials in order to identify ascent directions (see next paragraph). Even though Proposition 4.1 describes explicitly the whole superdifferential, finding an effective ascent direction in practice is made ... Voir le document complet

... target image in the presence of degradations ...literature. Image reconstruction problems are often formulated into the Maximum A Posteriori (MAP) ...simple and offers significant flexibility in the ... Voir le document complet

... L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignemen[r] ... Voir le document complet

... baseline for color correction, since it provides a stable common reference point for appearance across input views without view-dependent ...color for each vertex and is typically free from ... Voir le document complet

... 3 Numerical experiments In order to illustrate the behavior of the model, we consider in this section three applications: im- age inpainting, zooming and denoising. We evalu- ate the ability of the method to ... Voir le document complet

... This paper proposes accelerated subspace optimization methods in the context of image restoration. Subspace optimization methods belong to the class of iterative descent algorithms for unconstrained ... Voir le document complet

... science and engineering. However, the design of optical systems and image processing techniques for subsea environment are challenging tasks due to water ...of image degradation as it ... Voir le document complet

... [3] and deep networks [6], [13], ...good restoration performance, but are heavily dependent on the amount of training data available for each degradation ...generic image restoration ... Voir le document complet