Single depth image super resolution via a dual sparsity model
Yulun Zhang, Yongbing Zhang, Qionghai Dai · 2015
Depth images play an important role and are popularly used in many computer vision tasks recently. However, the limited resolution of the depth image has been hindering its further applications. To address this problem, we propose a novel dual sparsity model based single depth image super resolution algorithm, with a single low-resolution depth image as input. We formulate this problem by combining the recently developed analysis model and synthesis model exploiting the sparsity of analyzed vectors and the sparse coefficients respectively. The analysis operator and dictionaries are trained over extensive samples separately. We show that our model clearly outperforms state-of-the-art methods on the widely used Middlebury 2007 datasets both quantitatively and visually.