Depth image super-resolution using internal and external information
Haoheng Zheng, Abdesselam Bouzerdoum, Son Lam Phung
Abstract
The fast development of 3-D imaging techniques has increased demands for high-resolution depth images. Conventional depth super-resolution methods reconstruct the high-resolution image by accessing high frequency information, either internally from a high-resolution intensity image or externally from a high-resolution image database. In this paper, a new depth super-resolution method based on joint regularization is proposed, which exploits both internal and external high frequency information. Specifically, a joint regularization problem with different constraints is formulated, which allows us to solve for the high-resolution image and a sparse code simultaneously. These constraints are constructed by utilizing information from both internal and external high-frequency sources. Experimental evaluation suggests that the proposed method provides improved results over existing approaches, in terms of both visual appearance and objective image quality.
BibTeX
@inproceedings{icassp2015_depthimagesuperr,
title = {Depth image super-resolution using internal and external information},
author = {Haoheng Zheng and Abdesselam Bouzerdoum and Son Lam Phung},
booktitle = {ICASSP 2015},
year = {2015}
}