Basis Pursuit With Sparsity Averaging for Compressive Sampling of Iris Images

Rahim, Tariq; Magdalena, Rita; Pratama, I Putu Agus Eka; Novamizanti, Ledya; Ramatryana, I Nyoman Apraz; Shin, Soo Young; Kim, Dong-Seong

doi:10.1109/access.2022.3140429

Cited by 4 publications

(9 citation statements)

References 44 publications

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“…This section presents the results of experiment scenario from Section V-B to show the performance of RGB-TV. The performance between the proposed RGB-TV, RGB-BPSA [34], RGB-BP with state-of-the-arts with Haar basis [20], Daubechies 8 (Db8) basis, and curvelet basis are compared.…”

Section: Experiments Resultsmentioning

confidence: 99%

“…Furthermore, a TV-based SARA was proposed for CT images was proposed to reduce the processing time of BP in SARA [37]. Last, MIC for the retinal images was proposed by using CS framework based on BP and average sparsity model [34].…”

Section: Related Workmentioning

confidence: 99%

“…In this paper, real CRI data is considered from patient at TelkomMedika hospital, Bandung, Indonesia. The performance between the proposed RGB-TV, RGB-BPSA [34], RGB-BP with state-of-the-arts with Haar basis [20], Daubechies 8 (Db8) basis, and curvelet basis are compared.…”

Section: A Crimentioning

confidence: 99%

“…Fig. 4 shows the effect of CR to SNR results of proposed RGB-TV, RGB-BPSA [34], RGB-BP-Haar [20], RGB-BP-Db8, and RGB-BP-Curvelet. X and Y-value refer to CR = 2, 4, 6, 8, 10 and SNR in dB, respectively.…”

Section: A Compression Ratio (Cr)mentioning

confidence: 99%

“…An initial CMI framework in color medical images that considers RGB format was proposed using sparsity averaging (SA) reweighted analysis (RA) and referred to as RGB-SARA [33]. In addition, the CMI of color iris/retinal images using basis pursuit (BP) with SA (RGB-BPSA) [34] was proposed. The previous RGB-based CMI mostly based on BP where ℓ 1 normalization is exploited for CS reconstruction, while total variant (TV)-based CMI for color medical image is not proposed and investigated yet.…”

Section: Introductionmentioning

confidence: 99%

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Compressive Sampling of Color Retinal Image Using Spread Spectrum Fourier Sampling and Total Variant

et al. 2022

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This paper proposes compressive sampling (CS) framework for color retinal image (CRI) compression, which relies on spread spectrum Fourier sampling (SSFS) and total variant (TV)-based reconstruction method with three loop of RGB color space, referred to as RGB-TV. In CS, two steps of process are performed, i.e., compression and CS reconstruction. In compression steps, SFFS is performed to get compressed signal from original CRI with a high compression ratio (CR). While in CS reconstruction, TV-norm and TV proximal operator are exploited for problem optimization to recover original CRI from a compressed signal. In addition, signal-to-noise ratio (SNR), structural similarity (SSIM), and processing time are investigated for the performance metrics of the proposed RGB-TV. The computer simulation result shows that the proposed RGB-TV with a set of CRI of size 512 by 512 can compressed until CR = 10 which obtains mean SNR 22 dB, SSIM 0.84, and processing time 2.2 seconds. INDEX TERMSColor retinal image, compressive sampling (CS), RGB, spread spectrum (SS), total variant (TV).

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Section: Experiments Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: A Crimentioning

confidence: 99%

Section: A Compression Ratio (Cr)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Compressive Sampling of Color Retinal Image Using Spread Spectrum Fourier Sampling and Total Variant

et al. 2022

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

et al. 2022

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Bulletin of the Polish Academy of Sciences: Technical Sciences

Puchala

2022

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Discrete two-dimensional orthogonal wavelet transforms find applications in many areas of analysis and processing of digital images. In a typical scenario the separability of two-dimensional wavelet transforms is assumed and all calculations follow the row-column approach using one-dimensional transforms. For the calculation of one-dimensional transforms the lattice structures, which can be characterized by high computational efficiency and non-redundant parametrization, are often used. In this paper we show that the row-column approach can be excessive in the number of multiplications and rotations. Moreover, we propose the novel approach based on natively two-dimensional base operators which allows for significant reduction in the number of elementary operations, i.e., more than twofold reduction in the number of multiplications and fourfold reduction of rotations. The additional computational costs that arise instead include an increase in the number of additions, and introduction of bit-shift operations. It should be noted, that such operations are significantly less demanding in hardware realizations than multiplications and rotations. The performed experimental analysis proves the practical effectiveness of the proposed approach.

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Basis Pursuit With Sparsity Averaging for Compressive Sampling of Iris Images

Cited by 4 publications

References 44 publications

Compressive Sampling of Color Retinal Image Using Spread Spectrum Fourier Sampling and Total Variant

Compressive Sampling of Color Retinal Image Using Spread Spectrum Fourier Sampling and Total Variant

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

Bulletin of the Polish Academy of Sciences: Technical Sciences

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