Block-Based Compressive Sensed Thermal Image Reconstruction using Greedy Algorithms

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Usham V. Dias 1,* Milind E. Rane 2

1. Dept. of Electronics & Telecommunication, Padre Conceicao College of Engineering, Goa, India

2. Dept. of Electronics & Telecommunication, Vishwakarma Institute of Technology, Pune, India

* Corresponding author.


Received: 14 Mar. 2014 / Revised: 13 May 2014 / Accepted: 7 Aug. 2014 / Published: 8 Sep. 2014

Index Terms

Compressive sensing, greedy reconstruction, sensing pattern, Regularized Orthogonal Matching Pursuit


This paper implements a block based compressive sensing technique for thermal image reconstruction using greedy algorithms. A total of fourteen different sensing patterns were tested for data acquisition. Orthogonal Matching Pursuit (OMP) and Regularized Orthogonal Matching Pursuit (ROMP) with two different thresholds were implemented for image reconstruction with OMP having an edge over ROMP in terms of error and PSNR. ROMP was faster in terms of iterations needed for reconstruction. As the threshold for ROMP was increased the number of iterations needed decreased. Gaussian, Bernoulli and Hadamard patterns were the best for reconstruction. Hadamard matrix, Bernoulli matrix with +/-1 entries and Bernoulli matrix with 0/1 entries have the added advantage of being more conducive for hardware implementation. This paper used Discrete Cosine Transform as the sparsifying basis for reconstruction.

Cite This Paper

Usham V. Dias, Milind E. Rane,"Block-Based Compressive Sensed Thermal Image Reconstruction using Greedy Algorithms", IJIGSP, vol.6, no.10, pp.36-42, 2014. DOI: 10.5815/ijigsp.2014.10.05


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