Depth image super resolution using internal and e ternal information



Publication Details

H. Zheng, A. Bouzerdoum & S. Lam. Phung, "Depth image super resolution using internal and e ternal information," in Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on, 2015, pp. 1206-1210.


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.

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