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Compressive Imaging: Hybrid Projection Design Amit Ashok1

Summary: Compressive Imaging: Hybrid Projection Design
Amit Ashok1
and Mark A. Neifeld1,2
Department of Electrical and Computer Engineering1
College of Optical Science2
University of Arizona, Tucson, AZ, 85721, U.S.A.
email: ashoka@ece.arizona.edu
Abstract: Compressive imaging/sensing employing a random measurement basis does not incorporate the
specific object prior information available for natural images. An alternate hybrid measurement basis is
proposed that yields improved reconstruction performance for natural images.
2010 Optical Society of America
OCIS codes: (110.1758) Computational imaging; (100.3020) Image reconstruction-restoration
1. Introduction
Compressive imaging (sometimes also referred to as feature-specific imaging) exploits the redundancy inherent in
natural scenes to make compressive measurements towards the goal of image formation [1-4]. The compressed
sensing theory shows that a random measurement basis can achieve exact reconstruction given the object is sparse in
some transform basis even when the number of measurements is less than the object dimensionality [5-7]. This
elegant theory yields important generalized results, however, in the case of images of natural scenes it does not
utilize any additional object prior information available beyond sparsity. This motivates the design of a
measurement basis that incorporates this prior information specific to images of natural scenes and results in


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


Collections: Engineering