Abstract
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.
Highlights
Medical Imaging (MI) is the science of interpreting or investigating medical obstacles based on several MI processes and digital image processing methods [1]
With the advancement of medical diagnostic equipment, such as magnetic resonance imaging (MRI), ultrasound imaging (UI), computed tomography (CT), iris eye imaging, wireless capsule endoscopy (WCE), and other characteristic medical images are produced in the field of biomedical [2]
The results show that when l increases, signal-to-noise ratio (SNR) increases
Summary
Medical Imaging (MI) is the science of interpreting or investigating medical obstacles based on several MI processes and digital image processing methods [1]. RELATED WORKS Lately, CS method has been researched briefly for the reconstruction of the signals/images and has achieved satisfactory results considering both theoretical perspectives and engineering applications These applications span from data like natural to medical to even hyperspectral images [5]. CS has been investigated for the reconstruction of hyperspectral images (HSI), which is based on a multi-type mixing representation conducted at the spectral sampling staging using the CS technique. A detailed CS approach based on multiple basis reweighted analysis was proposed for medical data [5]. An RGB-based CMI using spread spectrum (SS) and BP with sparsity averaging (RGB-BPSA) for iris images are proposed in this paper. A novel compressed sampling for colored iris image is proposed by exploiting basis pursuit reconstruction method with average sparsity model.
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