Depth image super-resolution using multi-dictionary sparse representation
RIS ID
89438
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.
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.