DEPTH IMAGE SUPER-RESOLUTION USING MULTI-DICTIONARY SPARSE REPRESENTATION

被引：0

作者：

Zheng, H. ^{[1
]}

Bouzerdoum, A. ^{[1
]}

Phung, S. L. ^{[1
]}

机构：

[1] Univ Wollongong, Sch Elect Comp & Telecommun Engn, Wollongong, NSW 2522, Australia

来源：

2013 20TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP 2013) | 2013年

关键词：

depth super-resolution; sparse representation; multiple dictionaries; dictionary selection; basis pursuit; INTERPOLATION;

D O I：

暂无

中图分类号：

TB8 [摄影技术];

学科分类号：

0804 ;

摘要：

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.

引用

页码：957 / 961

页数：5

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