The tendency of natural scenes to cluster around low frequencies is not only useful in image compression, it also can prove advantageous in novel infrared and hyperspectral image acquisition. In this paper, we exploit this signal model with two approaches to enhance the quality of compressive imaging as implemented in a single-pixel compressive camera and compare these results against purely random acquisition. We combine projection patterns that can efficiently extract the model-based information with subsequent random projections to form the hybrid pattern sets. With the first approach, we generate low-frequency patterns via a direct transform. As an alternative, we also used principal component analysis of an image library to identify the low-frequency components. We present the first (to the best of our knowledge) experimental validation of this hybrid signal model on real data. For both methods, we acquire comparable quality of reconstructions while acquiring only half the number of measurements needed by traditional random sequences. The optimal combination of hybrid patterns and the effects of noise on image reconstruction are also discussed.
© 2014 Optical Society of America
Original Manuscript: December 30, 2013
Revised Manuscript: March 12, 2014
Manuscript Accepted: April 15, 2014
Published: July 9, 2014
Yun Li, Aswin C. Sankaranarayanan, Lina Xu, Richard Baraniuk, and Kevin F. Kelly, "Realization of hybrid compressive imaging strategies," J. Opt. Soc. Am. A 31, 1716-1720 (2014)