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arXiv · 1304.5319

A Joint Intensity and Depth Co-Sparse Analysis Model for Depth Map Super-Resolution

Martin Kiechle·Simon Hawe·Martin Kleinsteuber
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Abstract

High-resolution depth maps can be inferred from low-resolution depth measurements and an additional high-resolution intensity image of the same scene. To that end, we introduce a bimodal co-sparse analysis model, which is able to capture the interdependency of registered intensity and depth information. This model is based on the assumption that the co-supports of corresponding bimodal image structures are aligned when computed by a suitable pair of analysis operators. No analytic form of such operators exist and we propose a method for learning them from a set of registered training signals. This learning process is done offline and returns a bimodal analysis operator that is universally applicable to natural scenes. We use this to exploit the bimodal co-sparse analysis model as a prior for solving inverse problems, which leads to an efficient algorithm for depth map super-resolution.

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Martin Kiechle, Simon Hawe, Martin Kleinsteuber. 2013-04-19. A Joint Intensity and Depth Co-Sparse Analysis Model for Depth Map Super-Resolution. https://arxiv.org/abs/1304.5319

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