Compressive Sensing and Recovery for Binary Images

We propose a method for compressive sensing and recovery of binary images. To achieve this, we combine two ideas: in the sensing step, ordered aperture patterns are employed instead of random aperture patterns, and in the recovery step, a dense reconstruction scheme replaces sparse reconstruction. W...

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Bibliographic Details
Published in:IEEE transactions on image processing Vol. 25; no. 10; pp. 4796 - 4802
Main Author: Ahn, Jong-Hoon
Format: Journal Article
Language:English
Published: New York IEEE 01.10.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Apertures
binary image
Compressed sensing
Compressive sensing
Detection
Hadamard matrix
Image reconstruction
L^\infty norm
Minimization
Reconstruction
Recovery
Sensors
Sparse matrices
State of the art
Transaction processing
Transforms
ISSN:1057-7149, 1941-0042
Online Access:Get full text
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Summary:We propose a method for compressive sensing and recovery of binary images. To achieve this, we combine two ideas: in the sensing step, ordered aperture patterns are employed instead of random aperture patterns, and in the recovery step, a dense reconstruction scheme replaces sparse reconstruction. We demonstrate that this approach is more effective for binary images than the state-of-the-art algorithms relying on random sensing and sparse reconstruction.
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ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2016.2598651