Variational Depth Superresolution using Example-Based Edge Representations


In this work we propose a novel method for depth image superresolution which combines recent advances in example based upsampling with variational superresolution based on a known blur kernel. Most traditional depth superresolution approaches try to use additional high resolution intensity images as guidance for superresolution. In our method we learn a dictionary of edge priors from an external database of high and low resolution examples. In a novel variational sparse coding approach this dictionary is used to infer strong edge priors. Additionally to the traditional sparse coding constraints the difference in the overlap of neighboring edge patches is minimized in our optimization. These edge priors are used in a novel variational superresolution as anisotropic guidance of a higher order regularization. Both the sparse coding and the variational superresolution of the depth are solved based on the primal-dual formulation. In an exhaustive numerical and visual evaluation we show that our method clearly outperforms existing approaches on multiple real and synthetic datasets.
sdsr overview

Variational Depth Superresolution using Example-Based Edge Representations. Our method estimates strong edge priors from a given LR depth image and a learned dictionary using a novel sparse coding approach (blue part). The learned HR edge prior is used as anisotropic guidance in a novel variational SR using higher order regularization (red part).


David Ferstl

Matthias Rüther



Supplemental Material

Middlebury Evaluation Results

Laser Scan Evaluation Results


Conference Papers


  1. Variational Depth Superresolution using Example-Based Edge Representations  [bib] [supp] David Ferstl, Matthias Ruether, and Horst BischofIn Proceedings International Conference on Computer Vision (ICCV), IEEE, 2015