Compressed Sensing-Speech Coding Scheme for Mobile Communications

Haneche, Houria; Ouahabi, Abdeldjalil; Boudraa, Bachir

doi:10.1007/s00034-021-01712-x

Cited by 15 publications

(8 citation statements)

References 30 publications

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“…where ε > 0 is the noise level. For an appropriate scalar weight α, we can obtain the following variant of (6).…”

Section: Compressive Sensingmentioning

confidence: 99%

“…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%

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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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“…where ε > 0 is the noise level. For an appropriate scalar weight α, we can obtain the following variant of (6).…”

Section: Compressive Sensingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Image Denoising Using a Compressive Sensing Approach Based on Regularization Constraints

Mahdaoui

Ouahabi

Moulay

2022

Sensors

Self Cite

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“…Several approaches can improve the image quality during its transmission or storage [ 58 ]. We will mention, for example, those based on the concept of compressed sensing, where the image enhancement is carried out during acquisition [ 59 , 60 , 61 , 62 , 63 ]. An alternative approach is to perform simple and efficient de-noising (close to optimality) from first or second generation wavelets [ 64 , 65 , 66 ].…”

Section: Proposed Segmentation Methodsmentioning

confidence: 99%

Particle Swarm Optimization and Two-Way Fixed-Effects Analysis of Variance for Efficient Brain Tumor Segmentation

Atia

Benzaoui

Jacques

et al. 2022

Cancers

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Segmentation of brain tumor images, to refine the detection and understanding of abnormal masses in the brain, is an important research topic in medical imaging. This paper proposes a new segmentation method, consisting of three main steps, to detect brain lesions using magnetic resonance imaging (MRI). In the first step, the parts of the image delineating the skull bone are removed, to exclude insignificant data. In the second step, which is the main contribution of this study, the particle swarm optimization (PSO) technique is applied, to detect the block that contains the brain lesions. The fitness function, used to determine the best block among all candidate blocks, is based on a two-way fixed-effects analysis of variance (ANOVA). In the last step of the algorithm, the K-means segmentation method is used in the lesion block, to classify it as a tumor or not. A thorough evaluation of the proposed algorithm was performed, using: (1) a private MRI database provided by the Kouba imaging center—Algiers (KICA); (2) the multimodal brain tumor segmentation challenge (BraTS) 2015 database. Estimates of the selected fitness function were first compared to those based on the sum-of-absolute-differences (SAD) dissimilarity criterion, to demonstrate the efficiency and robustness of the ANOVA. The performance of the optimized brain tumor segmentation algorithm was then compared to the results of several state-of-the-art techniques. The results obtained, by using the Dice coefficient, Jaccard distance, correlation coefficient, and root mean square error (RMSE) measurements, demonstrated the superiority of the proposed optimized segmentation algorithm over equivalent techniques.

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“…He proposed using a low-speed A/D converter at the receiver as well as a restoration algorithm to reconstruct the target image [26]. This further promotes the process of imaging based on two-dimensional array radar and CS algorithm application on radar imaging area [27][28][29]. In 2010, Duan proposed the method of combining cross array MIMO radar with ISAR imaging technology to obtain 3D imaging results [30]; in 2011, Zhu designed a MIMO array for 3D imaging to achieve shorter time imaging [31]; and Gu combined MIMO technology and CS theory to propose a sparse L-shaped MIMO array single snapshot imaging algorithm [32].…”

Section: Introductionmentioning

confidence: 99%

MTRC-Tolerated Multi-Target Imaging Based on 3D Hough Transform and Non-Equal Sampling Sparse Solution

et al. 2021

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Distributed radar array brings several new forthcoming advantages in aerospace target detection and imaging. The two-dimensional distributed array avoids the imperfect motion compensation in coherent processing along slow time and can achieve single snapshot 3D imaging. Some difficulties exist in the 3D imaging processing. The first one is that the distributed array may be only in small amount. This means that the sampling does not meet the Nyquist sample theorem. The second one refers to echoes of objects in the same beam that will be mixed together, which makes sparse optimization dictionary too long for it to bring the huge computation burden in the imaging process. In this paper, we propose an innovative method on 3D imaging of the aerospace targets in the wide airspace with sparse radar array. Firstly, the case of multiple targets is not suitable to be processed uniformly in the imaging process. A 3D Hough transform is proposed based on the range profiles plane difference, which can detect and separate the echoes of different targets. Secondly, in the subsequent imaging process, considering the non-uniform sparse sampling of the distributed array in space, the migration through range cell (MTRC)-tolerated imaging method is proposed to process the signal of the two-dimensional sparse array. The uniformized method combining compressed sensing (CS) imaging in the azimuth direction and matched filtering in the range direction can realize the 3D imaging effectively. Before imaging in the azimuth direction, interpolation in the range direction is carried out. The main contributions of the proposed method are: (1) echo separation based on 3D transform avoids the huge amount of computation of direct sparse optimization imaging of three-dimensional data, and ensures the realizability of the algorithm; and (2) uniformized sparse solving imaging is proposed, which can remove the difficulty cause by MTRC. Simulation experiments verified the effectiveness and feasibility of the proposed method.

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Compressed Sensing-Speech Coding Scheme for Mobile Communications

Cited by 15 publications

References 30 publications

Image Denoising Using a Compressive Sensing Approach Based on Regularization Constraints

Image Denoising Using a Compressive Sensing Approach Based on Regularization Constraints

Particle Swarm Optimization and Two-Way Fixed-Effects Analysis of Variance for Efficient Brain Tumor Segmentation

MTRC-Tolerated Multi-Target Imaging Based on 3D Hough Transform and Non-Equal Sampling Sparse Solution

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