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2015 IEEE International Conference on Multimedia & Expo Workshops (ICMEW) (2015)
Turin, Italy
June 29, 2015 to July 3, 2015
ISBN: 978-1-4799-7079-7
pp: 1-6
Yulun Zhang , Graduate School at Shenzhen, Tsinghua University, China
Yongbing Zhang , Graduate School at Shenzhen, Tsinghua University, China
Qionghai Dai , Graduate School at Shenzhen, Tsinghua University, China
ABSTRACT
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.
INDEX TERMS
Analytical models, Image resolution, Dictionaries, Image reconstruction, Visualization, Image edge detection, DH-HEMTs
CITATION

Yulun Zhang, Yongbing Zhang and Qionghai Dai, "Single depth image super resolution via a dual sparsity model," 2015 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), Turin, Italy, 2015, pp. 1-6.
doi:10.1109/ICMEW.2015.7169851
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