Multiaperture imaging

Applied Optics
Premchandra M ShankarMark A Neifeld

Abstract

We study the reconstruction of a high-resolution image from multiple low-resolution images by using a nonlinear iterative backprojection algorithm. We exploit diversities in the imaging channels, namely, the number of imagers, magnification, position, rotation, and fill factor, to undo the degradation caused by the optical blur, pixel blur, and additive noise. We quantify the improvements gained by these diversities in the reconstruction process and discuss the trade-off among system parameters. As an example, for a system in which the pixel size is matched to the diffraction-limited optical blur size at a moderate detector noise level, we can reduce the reconstruction root-mean-square error by 570% by using 16 cameras and a large amount of diversity. The algorithm was implemented on a 56 camera array specifically constructed to demonstrate the resolution-enhancement capabilities. Practical issues associated with building and operating this device are presented and analyzed.

References

Dec 20, 2007·Optics Letters·M A Neifeld
Jan 1, 1997·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·M Elad, A Feuer

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Citations

Feb 24, 2007·Applied Optics·Premchandra M Shankar, Mark A Neifeld
Jun 23, 2009·Applied Optics·Guillaume DruartManuel Fendler
Feb 7, 2012·Applied Optics·Manjunath SomayajiDomhnull Granquist-Fraser
May 3, 2008·Journal of the Optical Society of America. A, Optics, Image Science, and Vision·Premchandra M Shankar, Mark A Neifeld
Apr 7, 2007·Applied Optics·Amit Ashok, Mark A Neifeld
Apr 2, 2010·Applied Optics·Vikrant R BhaktaMarc P Christensen

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