Enhanced RGB-Based Basis Pursuit Sparsity Averaging Using Variable Density Sampling for Compressive Sensing of Eye Images
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 recon...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9996363/ |
| _version_ | 1797974745341231104 |
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| author | Gandeva Bayu Satrya I. Nyoman Apraz Ramatryana Ledya Novamizanti Soo Young Shin |
| author_facet | Gandeva Bayu Satrya I. Nyoman Apraz Ramatryana Ledya Novamizanti Soo Young Shin |
| author_sort | Gandeva Bayu Satrya |
| collection | DOAJ |
| description | 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 an image with a resolution 512 <inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 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. |
| first_indexed | 2024-04-11T04:24:34Z |
| format | Article |
| id | doaj.art-120809695da9413e8aff8ccee1351f46 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T04:24:34Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-120809695da9413e8aff8ccee1351f462022-12-30T00:00:24ZengIEEEIEEE Access2169-35362022-01-011013343913345010.1109/ACCESS.2022.32313309996363Enhanced RGB-Based Basis Pursuit Sparsity Averaging Using Variable Density Sampling for Compressive Sensing of Eye ImagesGandeva Bayu Satrya0https://orcid.org/0000-0002-0243-9020I. Nyoman Apraz Ramatryana1https://orcid.org/0000-0001-5507-2579Ledya Novamizanti2https://orcid.org/0000-0001-6060-8243Soo Young Shin3https://orcid.org/0000-0002-2526-2395School of Applied Science, Telkom University, Bandung, IndonesiaDepartment of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, South KoreaSchool of Electrical Engineering and Humic Engineering, Telkom University, Bandung, IndonesiaDepartment of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi, South KoreaCompressive 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 an image with a resolution 512 <inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 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.https://ieeexplore.ieee.org/document/9996363/Compressive sensingsparsity averagingbasis pursuitcolor eye image |
| spellingShingle | Gandeva Bayu Satrya I. Nyoman Apraz Ramatryana Ledya Novamizanti Soo Young Shin Enhanced RGB-Based Basis Pursuit Sparsity Averaging Using Variable Density Sampling for Compressive Sensing of Eye Images IEEE Access Compressive sensing sparsity averaging basis pursuit color eye image |
| title | Enhanced RGB-Based Basis Pursuit Sparsity Averaging Using Variable Density Sampling for Compressive Sensing of Eye Images |
| title_full | Enhanced RGB-Based Basis Pursuit Sparsity Averaging Using Variable Density Sampling for Compressive Sensing of Eye Images |
| title_fullStr | Enhanced RGB-Based Basis Pursuit Sparsity Averaging Using Variable Density Sampling for Compressive Sensing of Eye Images |
| title_full_unstemmed | Enhanced RGB-Based Basis Pursuit Sparsity Averaging Using Variable Density Sampling for Compressive Sensing of Eye Images |
| title_short | Enhanced RGB-Based Basis Pursuit Sparsity Averaging Using Variable Density Sampling for Compressive Sensing of Eye Images |
| title_sort | enhanced rgb based basis pursuit sparsity averaging using variable density sampling for compressive sensing of eye images |
| topic | Compressive sensing sparsity averaging basis pursuit color eye image |
| url | https://ieeexplore.ieee.org/document/9996363/ |
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