Basis Pursuit With Sparsity Averaging for Compressive Sampling of Iris Images

This paper proposes novel compressive sampling (CS) of colored iris images using three RGB iterations of basis pursuit (BP) with sparsity averaging (SA), called RGB-BPSA. In RGB-BPSA, a sparsity basis is performed using an average of multiple coherent dictionaries to improve the performance of BP re...

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Bibliographic Details
Published in:IEEE access 2022, Vol.10, p.13728-13737
Main Authors: Rahim, Tariq, Magdalena, Rita, Pratama, I Putu Agus Eka, Novamizanti, Ledya, Ramatryana, I Nyoman Apraz, Shin, Soo Young, Kim, Dong Seong
Format: Article
Language:English
Subjects:
basis pursuit (BP)
Compressed sampling
Computed tomography
Image coding
Image reconstruction
Iris
iris images
Iris recognition
Magnetic resonance imaging
Medical diagnostic imaging
Reconstruction
Sampling
Signal quality
Signal to noise ratio
Sparsity
sparsity averaging
Visual signals
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Summary:This paper proposes novel compressive sampling (CS) of colored iris images using three RGB iterations of basis pursuit (BP) with sparsity averaging (SA), called RGB-BPSA. In RGB-BPSA, a sparsity basis is performed using an average of multiple coherent dictionaries to improve the performance of BP reconstruction. In the experiment, first, the level of wavelet decomposition is studied to analyze the best reconstruction result. Second, the effect of compression rate (CR) is considered. Third, the effect of resolution is investigated. Last, the sparse basis of SA is compared to the existing basis, i.e., curvelet, Daubechies-1 or haar, and Daubechies-8. The superior RGB-BPSA over existing CS is shown by better visual quality with a higher signal-to-noise ratio (SNR) and structural similarity (SSIM) index in the same CR. In addition, reconstruction time also investigated where RGB-BPSA outperforms the curvelet.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3140429