scholarly journals 3D Imaging Algorithm for Down-Looking MIMO Array SAR Based on Bayesian Compressive Sensing

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Xiaozhen Ren ◽  
Lina Chen ◽  
Jing Yang
Keyword(s):  
Compressive Sensing ◽  
3D Imaging ◽  
Additive Noise ◽  
Signal Reconstruction ◽  
3D Images ◽  
High Quality Image ◽  
Quality Image ◽  
New Strategy ◽  
Noisy Measurements ◽  
Cross Track

Down-looking MIMO array SAR can reconstruct 3D images of the observed area in the inferior of the platform of the SAR and has wide application prospects. In this paper, a new strategy based on Bayesian compressive sensing theory is proposed for down-looking MIMO array SAR imaging, which transforms the cross-track imaging process of down-looking MIMO array SAR into the problem of sparse signal reconstruction from noisy measurements. Due to account for additive noise encountered in the measurement process, high quality image can be achieved. Simulation results indicate that the proposed method can provide better resolution and lower sidelobes compared to the conventional method.

scholarly journals Downward-Looking Linear Array 3D SAR Imaging Based on Multiple Measurement Vectors Model and Continuous Compressive Sensing

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Qi-yong Liu ◽  
Qun Zhang ◽  
Fu-fei Gu ◽  
Yi-chang Chen ◽  
Le Kang ◽  
...  
Keyword(s):  
Compressive Sensing ◽  
3D Imaging ◽  
Linear Array ◽  
Multiple Measurement ◽  
Memory Space ◽  
Multiple Measurement Vectors ◽  
Huge Data ◽  
Imaging Result ◽  
Sar Imaging ◽  
Cross Track

This paper concerns the problems of huge data and off-grid effect of cross-track direction in downward-looking linear array (DLLA) 3D SAR imaging. Since the 3D imaging needs a great deal of memory space, we consider the methods of downsampling to reduce the data quantity. In the azimuth direction, we proposed a method based on the multiple measurement vectors (MMV) model, which can enhance computational efficiency and elevate the performance of antinoise, to recover the signal. Further, in cross-track direction, since the resolution is restricted by the length of array, as well as platform size, the influence of off-grid effect is more serious than azimuth direction. Continuous compressive sensing (CCS), which can solve the off-grid effect of the classical compressive sensing (CS), is presented to obtain the precise imaging result under the noise scenarios. Finally, we validate our method by extension numerical experiments.

IMAGE RECONSTRUCTION BASED ON COMPRESSIVE SAMPLING USING IRLS AND OMP ALGORITHM

2016 ◽  
Vol 78 (5) ◽  
Author(s):  
Indrarini Dyah Irawati ◽  
Andriyan B. Suksmono
Keyword(s):  
Image Reconstruction ◽  
Compressive Sensing ◽  
Wavelet Transformation ◽  
Matching Pursuit ◽  
Sampling Rate ◽  
Iteratively Reweighted Least Squares ◽  
High Quality ◽  
High Quality Image ◽  
Quality Image ◽  
Test Result

We proposed compressive sensing to reduce the sampling rate of the image and improve the accuracy of image reconstruction. Compressive sensing requires that the representation of the image is sparse on a certain basis. We use wavelet transformation to provide sparsity matrix basis. Meanwhile, to get a projection matrix using a random orthonormal process. The algorithm used to reconstruct the image is orthogonal matching pursuit (OMP) and Iteratively Reweighted Least Squares (IRLS). The test result indicates that a high quality image is obtained along with the number of coefficients M. IRLS has a good performance on PSNR than OMP while OMP takes the least time for reconstruction.

scholarly journals Spectral Tomography for 3D Element Detection and Mineral Analysis

Minerals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 598
Author(s):  
Jose R. A. Godinho ◽  
Gabriel Westaway-Heaven ◽  
Marijn A. Boone ◽  
Axel D. Renno
Keyword(s):  
Energy Spectrum ◽  
3D Imaging ◽  
X Rays ◽  
3D Images ◽  
Characterization Methods ◽  
X Ray ◽  
Additional Information ◽  
Mineral Phases ◽  
Spectral Computed Tomography

This paper demonstrates the potential of a new 3D imaging technique, Spectral Computed Tomography (sp-CT), to identify heavy elements inside materials, which can be used to classify mineral phases. The method combines the total X-ray transmission measured by a normal polychromatic X-ray detector, and the transmitted X-ray energy spectrum measured by a detector that discriminates between X-rays with energies of about 1.1 keV resolution. An analysis of the energy spectrum allows to identify sudden changes of transmission at K-edge energies that are specific of each element. The additional information about the elements in a phase improves the classification of mineral phases from grey-scale 3D images that would be otherwise difficult due to artefacts or the lack of contrast between phases. The ability to identify the elements inside the minerals that compose ore particles and rocks is crucial to broaden the application of 3D imaging in Earth sciences research and mineral process engineering, which will represent an important complement to traditional 2D imaging mineral characterization methods. In this paper, the first applications of sp-CT to classify mineral phases are showcased and the limitations and further developments are discussed.

scholarly journals Compressive Sensing in Signal Processing: Algorithms and Transform Domain Formulations

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Irena Orović ◽  
Vladan Papić ◽  
Cornel Ioana ◽  
Xiumei Li ◽  
Srdjan Stanković
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Compressive Sensing ◽  
Signal Reconstruction ◽  
Signal Acquisition ◽  
Transform Domain ◽  
Hermite Transform ◽  
Time Frequency ◽  
Reconstruction Methods ◽  
Fourier Transform Domain

Compressive sensing has emerged as an area that opens new perspectives in signal acquisition and processing. It appears as an alternative to the traditional sampling theory, endeavoring to reduce the required number of samples for successful signal reconstruction. In practice, compressive sensing aims to provide saving in sensing resources, transmission, and storage capacities and to facilitate signal processing in the circumstances when certain data are unavailable. To that end, compressive sensing relies on the mathematical algorithms solving the problem of data reconstruction from a greatly reduced number of measurements by exploring the properties of sparsity and incoherence. Therefore, this concept includes the optimization procedures aiming to provide the sparsest solution in a suitable representation domain. This work, therefore, offers a survey of the compressive sensing idea and prerequisites, together with the commonly used reconstruction methods. Moreover, the compressive sensing problem formulation is considered in signal processing applications assuming some of the commonly used transformation domains, namely, the Fourier transform domain, the polynomial Fourier transform domain, Hermite transform domain, and combined time-frequency domain.

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