l1 Regularized Super-resolution From Unregistered Omnidirectional Images

In this paper, we address the problemof super-resolution from multiple low-resolution omnidirectional images with inexact registration. Such a problem is typically encountered in omnidirectional vision scenarios with reduced resolution sensors in imperfect settings. Several spherical images with arbitrary rotations in the SO(3) rotation group are used for the reconstruction of higher resolution images. We propose an l1 regularized total least squares normminimization method for joint registration and reconstruction with better stabilization and denoising. Experimental results show that regularization offers a quality improvement of up to 1dB. In addition, it reduces the number of low resolution images that are necessary to reconstruct a high resolution image at a target quality.


Published in:
Proceedings of ICASSP
Presented at:
34th International Conference on Acoustics, Speech, and Signal Processing, Taipei, Taiwan, April 19-24, 2009
Year:
2009
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 Record created 2008-12-17, last modified 2018-03-17

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