HIERARCHICAL EXAMPLE-BASED RANGE-IMAGE SUPER-RESOLUTION WITH EDGE-PRESERVATION

We propose an example-based approach for enhancing resolution of range-images. Unlike most existing methods on range-image super-resolution (SR), we do not employ a colour image counterpart for the range-image. Moreover, we use only a small set of range-images to construct a dictionary of exemplars. Considering the importance of edges in range-image SR, our formulation involves an edge-based constraint to better weight appropriate patches from the dictionary in a sparse-representation framework. Moreover, realizing the need for large up-sampling factors in case of range-images, we follow a hierarchical strategy for estimating the high-resolution range-images. We demonstrate that our strategy yields considerable improvements over the state-of-the-art approaches for range-image SR.