A Joint Intensity and Depth Co-sparse Analysis Model for Depth Map Super-resolution

Martin Kiechle, Simon Hawe, Martin Kleinsteuber; The IEEE International Conference on Computer Vision (ICCV), 2013, pp. 1545-1552

Abstract


High-resolution depth maps can be inferred from lowresolution depth measurements and an additional highresolution 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.

Related Material


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[bibtex]
@InProceedings{Kiechle_2013_ICCV,
author = {Kiechle, Martin and Hawe, Simon and Kleinsteuber, Martin},
title = {A Joint Intensity and Depth Co-sparse Analysis Model for Depth Map Super-resolution},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2013}
}