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 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.