535 research outputs found
Compressive sensing for 3D microwave imaging systems
Compressed sensing (CS) image reconstruction techniques are developed and experimentally implemented for wideband microwave synthetic aperture radar (SAR) imaging systems with applications to nondestructive testing and evaluation. These techniques significantly reduce the number of spatial measurement points and, consequently, the acquisition time by sampling at a level lower than the Nyquist-Shannon rate. Benefiting from a reduced number of samples, this work successfully implemented two scanning procedures: the nonuniform raster and the optimum path.
Three CS reconstruction approaches are also proposed for the wideband microwave SAR-based imaging systems. The first approach reconstructs a full-set of raw data from undersampled measurements via L1-norm optimization and consequently applies 3D forward SAR on the reconstructed raw data. The second proposed approach employs forward SAR and reverse SAR (R-SAR) transforms in each L1-norm optimization iteration reconstructing images directly. This dissertation proposes a simple, elegant truncation repair method to combat the truncation error which is a critical obstacle to the convergence of the CS iterative algorithm. The third proposed CS reconstruction algorithm is the adaptive basis selection (ABS) compressed sensing. Rather than a fixed sparsifying basis, the proposed ABS method adaptively selects the best basis from a set of bases in each iteration of the L1-norm optimization according to a proposed decision metric that is derived from the sparsity of the image and the coherence between the measurement and sparsifying matrices. The results of several experiments indicate that the proposed algorithms recover 2D and 3D SAR images with only 20% of the spatial points and reduce the acquisition time by up to 66% of that of conventional methods while maintaining or improving the quality of the SAR images --Abstract, page iv
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)
The implicit objective of the biennial "international - Traveling Workshop on
Interactions between Sparse models and Technology" (iTWIST) is to foster
collaboration between international scientific teams by disseminating ideas
through both specific oral/poster presentations and free discussions. For its
second edition, the iTWIST workshop took place in the medieval and picturesque
town of Namur in Belgium, from Wednesday August 27th till Friday August 29th,
2014. The workshop was conveniently located in "The Arsenal" building within
walking distance of both hotels and town center. iTWIST'14 has gathered about
70 international participants and has featured 9 invited talks, 10 oral
presentations, and 14 posters on the following themes, all related to the
theory, application and generalization of the "sparsity paradigm":
Sparsity-driven data sensing and processing; Union of low dimensional
subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph
sensing/processing; Blind inverse problems and dictionary learning; Sparsity
and computational neuroscience; Information theory, geometry and randomness;
Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?;
Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website:
http://sites.google.com/site/itwist1
Sparse nonlinear optimization for signal processing and communications
This dissertation proposes three classes of new sparse nonlinear optimization algorithms for network echo cancellation (NEC), 3-D synthetic aperture radar (SAR) image reconstruction, and adaptive turbo equalization in multiple-input multiple-output (MIMO) underwater acoustic (UWA) communications, respectively.
For NEC, the proposed two proportionate affine projection sign algorithms (APSAs) utilize the sparse nature of the network impulse response (NIR). Benefiting from the characteristics of l₁-norm optimization, affine projection, and proportionate matrix, the new algorithms are more robust to impulsive interferences and colored input than the conventional adaptive algorithms.
For 3-D SAR image reconstruction, the proposed two compressed sensing (CS) approaches exploit the sparse nature of the SAR holographic image. Combining CS with the range migration algorithms (RMAs), these approaches can decrease the load of data acquisition while recovering satisfactory 3-D SAR image through l₁-norm optimization.
For MIMO UWA communications, a robust iterative channel estimation based minimum mean-square-error (MMSE) turbo equalizer is proposed for large MIMO detection. The MIMO channel estimation is performed jointly with the MMSE equalizer and the maximum a posteriori probability (MAP) decoder. The proposed MIMO detection scheme has been tested by experimental data and proved to be robust against tough MIMO channels. --Abstract, page iv
Sparse Signal Models for Data Augmentation in Deep Learning ATR
Automatic Target Recognition (ATR) algorithms classify a given Synthetic
Aperture Radar (SAR) image into one of the known target classes using a set of
training images available for each class. Recently, learning methods have shown
to achieve state-of-the-art classification accuracy if abundant training data
is available, sampled uniformly over the classes, and their poses. In this
paper, we consider the task of ATR with a limited set of training images. We
propose a data augmentation approach to incorporate domain knowledge and
improve the generalization power of a data-intensive learning algorithm, such
as a Convolutional neural network (CNN). The proposed data augmentation method
employs a limited persistence sparse modeling approach, capitalizing on
commonly observed characteristics of wide-angle synthetic aperture radar (SAR)
imagery. Specifically, we exploit the sparsity of the scattering centers in the
spatial domain and the smoothly-varying structure of the scattering
coefficients in the azimuthal domain to solve the ill-posed problem of
over-parametrized model fitting. Using this estimated model, we synthesize new
images at poses and sub-pixel translations not available in the given data to
augment CNN's training data. The experimental results show that for the
training data starved region, the proposed method provides a significant gain
in the resulting ATR algorithm's generalization performance.Comment: 12 pages, 5 figures, to be submitted to IEEE Transactions on
Geoscience and Remote Sensin
SPHR-SAR-Net: Superpixel High-resolution SAR Imaging Network Based on Nonlocal Total Variation
High-resolution is a key trend in the development of synthetic aperture radar
(SAR), which enables the capture of fine details and accurate representation of
backscattering properties. However, traditional high-resolution SAR imaging
algorithms face several challenges. Firstly, these algorithms tend to focus on
local information, neglecting non-local information between different pixel
patches. Secondly, speckle is more pronounced and difficult to filter out in
high-resolution SAR images. Thirdly, the process of high-resolution SAR imaging
generally involves high time and computational complexity, making real-time
imaging difficult to achieve. To address these issues, we propose a Superpixel
High-Resolution SAR Imaging Network (SPHR-SAR-Net) for rapid despeckling in
high-resolution SAR mode. Based on the concept of superpixel techniques, we
initially combine non-convex and non-local total variation as compound
regularization. This approach more effectively despeckles and manages the
relationship between pixels while reducing bias effects caused by convex
constraints. Subsequently, we solve the compound regularization model using the
Alternating Direction Method of Multipliers (ADMM) algorithm and unfold it into
a Deep Unfolded Network (DUN). The network's parameters are adaptively learned
in a data-driven manner, and the learned network significantly increases
imaging speed. Additionally, the Deep Unfolded Network is compatible with
high-resolution imaging modes such as spotlight, staring spotlight, and sliding
spotlight. In this paper, we demonstrate the superiority of SPHR-SAR-Net
through experiments in both simulated and real SAR scenarios. The results
indicate that SPHR-SAR-Net can rapidly perform high-resolution SAR imaging from
raw echo data, producing accurate imaging results
A Novel Compressed Sensing Method for Magnetic Resonance Imaging: Exponential Wavelet Iterative Shrinkage-Thresholding Algorithm with Random Shift
Aim. It can help improve the hospital throughput to accelerate magnetic resonance imaging (MRI) scanning. Patients will benefit from less waiting time. Task. In the last decade, various rapid MRI techniques on the basis of compressed sensing (CS) were proposed. However, both computation time and reconstruction quality of traditional CS-MRI did not meet the requirement of clinical use.
Method. In this study, a novel method was proposed with the name of exponential wavelet iterative shrinkagethresholding algorithm with random shift (abbreviated as EWISTARS). It is composed of three successful components: (i) exponential wavelet transform, (ii) iterative shrinkage-thresholding algorithm, and (iii) randomshift.
Results. Experimental results validated that, compared to state-of-the-art approaches, EWISTARS obtained the leastmean absolute error, the leastmean-squared error, and the highest peak signal-to-noise ratio.
Conclusion. EWISTARS is superior to state-of-the-art approaches
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