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Compressive imaging: hybrid measurement basis design Amit Ashok1,
 

Summary: Compressive imaging: hybrid measurement basis design
Amit Ashok1,
* and Mark A. Neifeld1,2
1
1230 East Speedway Boulevard, Department of Electrical and Computer Engineering,
University of Arizona, Tucson, Arizona 85721, USA
2
1630 East University Boulevard, College of Optics, University of Arizona, Tucson, Arizona 85721, USA
*Corresponding author: ashoka@ece.arizona.edu
Received February 2, 2011; accepted March 9, 2011;
posted March 25, 2011 (Doc. ID 142151); published May 17, 2011
The inherent redundancy in natural scenes forms the basis of compressive imaging where the number of measure-
ments is less than the dimensionality of the scene. The compressed sensing theory has shown that a purely random
measurement basis can yield good reconstructions of sparse objects with relatively few measurements. However,
additional prior knowledge about object statistics that is typically available is not exploited in the design of
the random basis. In this work, we describe a hybrid measurement basis design that exploits the power spectral
density statistics of natural scenes to minimize the reconstruction error by employing an optimal combination of a
nonrandom basis and a purely random basis. Using simulation studies, we quantify the reconstruction error
improvement achievable with the hybrid basis for a diverse set of natural images. We find that the hybrid basis
can reduce the reconstruction error up to 77% or equivalently requires fewer measurements to achieve a desired

  

Source: Ashok, Amit - Department of Electrical and Computer Engineering, University of Arizona

 

Collections: Engineering