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Disparity estimation constitutes an active research area in stereo vision, and in recent years, global estimation methods aiming at minimizing an energy function over the whole image have gained a lot of attention. To overcome the difficulties raised by the nonconvexity of the minimized criterion, convex relaxations have been proposed by several authors. In this paper, the global energy function is...
In this paper, we propose a new approach for estimating depth maps of stereo images which are prone to various types of noise. This method, based on a parallel proximal algorithm, gives a great flexibility in the choice of the constrained criterion to be minimized, thus allowing us to take into account different types of noise distributions. Our main objective is to present an iterative estimation...
Stereo matching is an active area of research in image processing. In a recent work, a convex programming approach was developed in order to generate a dense disparity field. In this paper, we address the same estimation problem and propose to solve it in a more general convex optimization framework based on proximal methods. More precisely, unlike previous works where the criterion must satisfy some...
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