Fast and accurate compressed sensing model in magnetic resonance imaging with median filter and split Bregman method

Yang, Yunyun; Qin, Xuxu; Wu, Boying

doi:10.1049/iet-ipr.2018.5173

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…Compressed sensing (CS) has already attracted great interest in various fields. Examples include medical imaging [ 1 , 2 ], communication systems [ 3 , 4 , 5 , 6 ], remote sensing [ 7 ], reconstruction algorithm design [ 8 ], image storage in databases [ 9 ], etc. Compressed sensing provides an alternative approach to Shannon’s vision to reduce the number of samples and/or reduce transmission/storage costs.…”

Section: Introductionmentioning

confidence: 99%

Image Denoising Using a Compressive Sensing Approach Based on Regularization Constraints

Mahdaoui

Ouahabi

Moulay

2022

Sensors

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In remote sensing applications and medical imaging, one of the key points is the acquisition, real-time preprocessing and storage of information. Due to the large amount of information present in the form of images or videos, compression of these data is necessary. Compressed sensing is an efficient technique to meet this challenge. It consists in acquiring a signal, assuming that it can have a sparse representation, by using a minimum number of nonadaptive linear measurements. After this compressed sensing process, a reconstruction of the original signal must be performed at the receiver. Reconstruction techniques are often unable to preserve the texture of the image and tend to smooth out its details. To overcome this problem, we propose, in this work, a compressed sensing reconstruction method that combines the total variation regularization and the non-local self-similarity constraint. The optimization of this method is performed by using an augmented Lagrangian that avoids the difficult problem of nonlinearity and nondifferentiability of the regularization terms. The proposed algorithm, called denoising-compressed sensing by regularization (DCSR) terms, will not only perform image reconstruction but also denoising. To evaluate the performance of the proposed algorithm, we compare its performance with state-of-the-art methods, such as Nesterov’s algorithm, group-based sparse representation and wavelet-based methods, in terms of denoising and preservation of edges, texture and image details, as well as from the point of view of computational complexity. Our approach permits a gain up to 25% in terms of denoising efficiency and visual quality using two metrics: peak signal-to-noise ratio (PSNR) and structural similarity (SSIM).

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