Enhanced RGB-Based Basis Pursuit Sparsity Averaging Using Variable Density Sampling for Compressive Sensing of Eye Images

Enhanced RGB-Based Basis Pursuit Sparsity Averaging Using Variable Density Sampling for Compressive Sensing of Eye Images

Blog Article

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 Personal Hygiene 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 an image with a resolution 512 $ imes $ 512 pixels into a Carbs and Fuel 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.