Open AccessEnhanced RGB-Based Basis Pursuit Sparsity Averaging Using Variable Density Sampling for Compressive Sensing of Eye Images
Author(s) -
Gandeva Bayu Satrya,
I Nyoman Apraz Ramatryana,
Ledya Novamizanti,
Soo Young Shin
Publication year - 2022
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2022.3231330
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Compressive sensing (CS) plays a critical role in sampling, transmitting, and storing the color medical image, i.e., magnetic resonance imaging, colonoscopy, wireless capsule endoscopy, and eye images. Although CS for medical images has been extensively investigated, a challenge remains in the reconstruction time of the CS. This paper considers a reconstruction of CS using sparsity averaging (SA)-based basis pursuit (BP) for RGB color space of eye image, referred to as RGB-BPSA. Next, an enhanced RGB-BPSA (E-RGB-BPSA) is proposed to reduce the reconstruction time of RGB-BPSA using a simple SA generated by the combination of Daubechies-1 and Daubechies-8 wavelet filters. In addition, variable density sampling is proposed for the measurement of E-RGB-BPSA. The performance metrics are investigated in terms of structural similarity (SSIM) index, signal-to-noise ratio (SNR), and CPU time. The simulation results show the superior E-RGB-BPSA over the existing RGB-BPSA at a CRI with a resolution 512×512 pixels into a measurement rate 10% with SSIM of 0.9, SNR of 20 dB, and CPU time of 20 seconds. The E-RGB-BPSA can be a solution to massive data transmissions and storage for the future of medical imaging.