## Sampling strategy for the sparse recovery of infrared images |

Applied Optics, Vol. 52, Issue 28, pp. 6858-6867 (2013)

http://dx.doi.org/10.1364/AO.52.006858

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### Abstract

The compressive sensing (CS) framework states that a signal that has a sparse representation in a known basis may be reconstructed from samples obtained at a sub-Nyquist sampling rate. The Fourier domain is widely used in CS applications due to its inherent properties. Sparse signal recovery applications using a small number of Fourier transform coefficients have made solutions to large-scale data recovery problems, including image recovery problems, more practical. The sparse reconstruction of 2D images is performed using the sampling patterns generated by taking the general frequency characteristics of the images into account. In this work, instead of forming a general sampling pattern for infrared (IR) images, a special sampling pattern is obtained by gathering a database to extract the frequency characteristics of IR sea-surveillance images. Experimental results show that the proposed sampling pattern provides better sparse recovery results compared to the widely used patterns proposed in the literature. It is also shown that, together with a certain image dataset, the sampling pattern generated by the proposed scheme can be generalized for various image sparse recovery applications.

© 2013 Optical Society of America

**OCIS Codes**

(070.0070) Fourier optics and signal processing : Fourier optics and signal processing

(100.2000) Image processing : Digital image processing

(100.3010) Image processing : Image reconstruction techniques

(100.3190) Image processing : Inverse problems

(110.3080) Imaging systems : Infrared imaging

(110.1758) Imaging systems : Computational imaging

**ToC Category:**

Image Processing

**History**

Original Manuscript: April 24, 2013

Revised Manuscript: August 6, 2013

Manuscript Accepted: August 15, 2013

Published: September 24, 2013

**Citation**

Serdar Cakir, Hande Uzeler, and Tayfun Aytaç, "Sampling strategy for the sparse recovery of infrared images," Appl. Opt. **52**, 6858-6867 (2013)

http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-52-28-6858

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