Depth image super-resolution using multi-dictionary sparse representation

Authors

Haoheng Zheng, University of WollongongFollow
Abdesselam Bouzerdoum, University of WollongongFollow
Son Lam Phung, University of WollongongFollow

RIS ID

89438

Publication Details

H. Zheng, A. Bouzerdoum & S. L. Phung, "Depth image super-resolution using multi-dictionary sparse representation," in 20th IEEE International Conference on Image Processing (ICIP 2013), 2013, pp. 957-961.

Abstract

In this paper, we propose a new depth super-resolution technique based on multiple dictionary learning. A novel dictionary selection method using basis pursuit is proposed to generate multiple dictionaries adaptively. A sparse representation of each low-resolution input patch is derived based on the learned dictionaries, and then used to reconstruct the corresponding high-resolution patch. Experimental results are presented which show that the proposed multi-dictionary scheme outperforms existing depth super-resolution methods.