Compressed medical imaging based on average sparsity model and reweighted analysis of multiple basis pursuit

Rahim, Tariq; Novamizanti, Ledya; Ramatryana, I Nyoman Apraz; Shin, Soo Young

doi:10.1016/j.compmedimag.2021.101927

Cited by 15 publications

(12 citation statements)

References 30 publications

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“…In addition, double concatenated SARA (DC-SARA) was proposed to enhance SARA by using a group of SARA basis and BP regularization for the reconstruction of the CS [36]. Next, a generalize version of DC-SARA was proposed and referred to as M-BRA [31]. Furthermore, a TV-based SARA was proposed for CT images was proposed to reduce the processing time of BP in SARA [37].…”

Section: Related Workmentioning

confidence: 99%

“…Recently, Recently, an application of CS for MIC using multiple dictionary of sparse basis was proposed, called compressed medical imaging (CMI) using multibasis reweighted analysis (M-BRA) [31]. CMI reduces the operational time of medical device with sparse acquisition process and store the samples in CS domain.…”

Section: Introductionmentioning

confidence: 99%

“…The transmission, storage, and representation issues in a smaller size with a high level of perceived quality also can be improved. In [31], four types of medical imaging are considered, i.e., computed tomography, wireless capsule endoscopy images, colonoscopy images, and magnetic resonance imaging, where only grayscale format is considered as the input of the proposed CMI. However, color medical images is quite sensitive, need to be of good quality to perform medical analysis in color feature, and require a CMI in color space (RGB format).…”

Section: Introductionmentioning

confidence: 99%

“…Throughout this paper, novel TV-based reconstruction method for RGB color retinal images (RGB-TV) is proposed. Different from RGB-SARA [33] and M-BRA [31], RA is not considered in this paper because RA process requires long processing time. This paper aims both processing time and visual quality improvement of the CMI.…”

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: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

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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“…Then, the original signal is recovered with a high probability from fewer observations through an optimization algorithm. Currently, commonly used compression and re-construction algorithms include greedy algorithm [ 10 ], convex optimization algorithm [ 11 ], Bayesian learning [ 12 ], etc. For example, Liu et al [ 13 ] used a low-pass filtering method to optimize the electrographic signal and used basis pursuit (BP) algorithm to compress and reconstruct the electrocardiogram signal.…”

Section: Introductionmentioning

confidence: 99%

Compression Reconstruction and Fault Diagnosis of Diesel Engine Vibration Signal Based on Optimizing Block Sparse Bayesian Learning

Bai

Wen

et al. 2022

Sensors

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It is critical to deploy wireless data transmission technologies remotely, in real-time, to monitor the health state of diesel engines dynamically. The usual approach to data compression is to collect data first, then compress it; however, we cannot ensure the correctness and efficiency of the data. Based on sparse Bayesian optimization block learning, this research provides a method for compression reconstruction and fault diagnostics of diesel engine vibration data. This method’s essential contribution is combining compressive sensing technology with fault diagnosis. To achieve a better diagnosis effect, we can effectively improve the wireless transmission efficiency of the vibration signal. First, the dictionary is dynamically updated by learning the dictionary using singular value decomposition to produce the ideal sparse form. Second, a block sparse Bayesian learning boundary optimization approach is utilized to recover structured non-sparse signals rapidly. A detailed assessment index of the data compression effect is created. Finally, the experimental findings reveal that the approach provided in this study outperforms standard compression methods in terms of compression efficiency and accuracy and its ability to produce the desired fault diagnostic effect, proving the usefulness of the proposed method.

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Reversible Image Authentication Scheme with Tampering Reconstruction Based on Very Deep Super Resolution Network

Melendez-Melendez

Cumplido

2022

Advances in Computational Intelligence

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Compressed medical imaging based on average sparsity model and reweighted analysis of multiple basis pursuit

Cited by 15 publications

References 30 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

Compression Reconstruction and Fault Diagnosis of Diesel Engine Vibration Signal Based on Optimizing Block Sparse Bayesian Learning

Reversible Image Authentication Scheme with Tampering Reconstruction Based on Very Deep Super Resolution Network

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