In this work we address the problem of solving ill-posed inverse problems in imaging where prior is a variational autoencoder (VAE). Specifically consider decoupled case trained once and can be reused for many different log-concave degradation models without retraining. Whereas previous MAP-based approaches to lead highly nonconvex optimization algorithms, our approach computes joint (space-latent) MAP that naturally leads alternate algorithms use stochastic encoder accelerate computations....

Binocular stereovision estimates the three-dimensional shape of a scene from two photographs taken different points view. In rectified epipolar geometry, this is equivalent to matching problem. This article describes method proposed by Kolmogorov and Zabih in 2001, which puts forward an energy-based formulation. The aim minimize four-term-energy. energy not convex cannot be minimized except among class perturbations called expansion moves, case exact minimization can done with graph cuts...

Estimating the depth, or equivalently disparity, of a stereo scene is challenging problem in computer vision. The method proposed by Rhemann et al. 2011 based on filtering cost volume, which gives for each pixel and hypothesized disparity derived from pixel-by-pixel comparison. performed guided filter He 2010. It computes weighted local average costs. weights are such that similar pixels tend to have Eventually, winner-take-all strategy selects with minimal pixel. Non-consistent labels...

Abstract This study examines whether institutional quality, education levels and trade openness affect the relationship between sectoral‐ industrial‐level foreign direct investment (FDI) inflows economic growth. Previous studies highlight heterogeneous effects of FDI at suggesting need to examine growth a disaggregated level. However, previous have not empirically explored role absorptive capacities industrial‐ sectoral‐level. Consequently, using sample 36 Organisation for Economic...

There has been increasing interest in constrained nonconvex regularized block optimization problems. We introduce an approach that enables complex application-dependent regularization terms to be used. The proposed alternating structure-adapted proximal gradient descent algorithm enjoys simple well-defined updates and is proved a value-convergent scheme general cases. Global convergence of the critical point using so-called Kurdyka--Łojasiewicz property.

In this paper we address the problem of solving ill-posed inverse problems in imaging where prior is a neural generative model. Specifically consider decoupled case trained once and can be reused for many different log-concave degradation models without retraining. Whereas previous MAP-based approaches to lead highly non-convex optimization algorithms, our approach computes joint (space-latent) MAP that naturally leads alternate algorithms use stochastic encoder accelerate computations. The...

In this paper, we propose a dense two-frame stereo algorithm which handles occlusion in variational framework. Our method is based on new regularization model includes both constraint the width and visibility nonoccluded areas. The minimization of resulting energy functional done by convex relaxation. A post-processing then detects fills occluded regions. We also novel dissimilarity measure that combines color gradient comparison with variable respective weight, to benefit from robustness...

In the present paper we propose a novel convergence analysis of Alternating Direction Methods Multipliers (ADMM), based on its equivalence with overrelaxed Primal-Dual Hybrid Gradient (oPDHG) algorithm. We consider smooth case, which correspond to cas where objective function can be decomposed into one differentiable Lipschitz continuous gradient part and strongly convex part. An accelerated variant ADMM is also proposed, shown converge linearly same rate as oPDHG.