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In this paper we address the problem of learning image structures directly from sparse codes. We first model images as linear combinations of molecules, which are themselves groups of atoms from a redundant dictionary. We then formulate a new structure learning problem that learns molecules directly from image sparse codes, namely from the image representation in the atom domain. We build on a structural...
This paper presents a novel algorithm for computing the relative motion between images from compressed linear measurements. We propose a geometry based correlation model that describes the relative motion between images by translational motion of visual features. We focus on the problem of estimating the motion field from a reference image and a highly compressed image given by means of random projections,...
This paper proposes a joint reconstruction algorithm for compressed correlated images that are given under the form of linear measurements. We first propose a geometry based model in order to describe the correlation between visual information in a pair of images, which is mostly driven by the translational motion of objects or vision sensors. We consider the particular problem where one image is...
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