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.
Kiechle et al. REF introduced a co-sparse analysis model to capture the interdependency of registered intensity and depth information.
A Joint Intensity and Depth Co-sparse Analysis Model for Depth Map Super-resolution
2013 IEEE International Conference on Computer Vision
2013 IEEE International Conference on Computer Vision
Computer Science