We report a method for super-resolution of range images. Our approach leverages the interpretation of LR image as sparse samples on the HR grid. Based on this interpretation, we demonstrate that our recently reported approach, which reconstructs dense range images from sparse range data by exploiting a registered colour image, can be applied for the task of resolution enhancement of range images. Our method only uses a single colour image in addition to the range observation in the super-resolution process. Using the proposed approach, we demonstrate super-resolution results for large factors (e.g. 4) with good localization accuracy.
The authors in @cite_10 propose an application of bilateral filtering which exploits constraints from the HR colour image. This work demonstrates ability to achieve good quality super-resolution by large factors. However, the bilateral filter, which is based on the HR colour image, has to be defined at each pixel. This makes the approach computationally very demanding. Our approach which works on image segments rather than pixels, and is based on computing local costs over segments, is a lot more efficient.
@cite_10
2104599718
We present a new post-processing step to enhance the resolution of range images. Using one or two registered and potentially high-resolution color images as reference, we iteratively refine the input low-resolution range image, in terms of both its spatial resolution and depth precision. Evaluation using the Middlebury benchmark shows across-the-board improvement for sub-pixel accuracy. We also demonstrated its effectiveness for spatial resolution enhancement up to 100 times with a single reference image.