Image guided depth map superresolution using non-local total generalized variation

Hai-Tao Zhang, Kai Kang, Zengfu Wang

Abstract: In order to solve the problem of depth map super-resolution, this paper proposes a novel approach to obtain super-resolution result from the raw depth map captured by a 3D camera under a convex optimization framework. In our method, non-local total generalized variation (NL-TGV) is utilized to measure the smoothness of depth map, and the data-fidelity term is expressed by the Huber norm. To preserve the sharpness of depth discontinuities, the smoothing weights are decided by the combination of bilateral weight and separating probability between pixels, and the histogram distance between pixels in the raw depth map is used to adjust the combination weights for suppressing the texture-transfer. We have derived a numerical solution scheme for the optimization problem using the first order primal dual algorithm. Quantitative and qualitative evaluations on the synthesis datasets and the real datasets demonstrate that our method is as good as the state-of-the-art approaches.