Three-Dimensional Sparse Turntable Microwave Imaging Based on Compressive Sensing

Microwave Imaging offers safe, low-cost, and portable method for medical imaging applications. These advantages make the microwave imaging convenient for early detection of tumor or cancer. The transmission method is one of the methods in microwave imaging which provides fast measurement and simple image reconstruction. However, this method requires a great number of measurements to obtain a well-reconstructed image. In order to reduce the number of measurements, this research proposes a Compressive Sensing (CS) approach for image reconstruction on microwave imaging. Compressive Sensing allows reconstruction of a signal with fewer measurements than the conventional approach. In this research, the scanning process is conducted on Computer Simulation Technology (CST) Microwave Studio software. Two dipole antennas with 3 GHz frequency are utilized as microwave transmitter and receiver. A two-layer cube phantom acts as the scanned object. Each layer has different relative permittivity which illustrates the healthy cell and abnormal cell. To meet the framework of Compressive Sensing, a weighted matrix of Discrete Radon Transform (DRT) is created as a projection matrix which delineates the data acquisition scheme in the scanning process. Discrete Cosine Transform (DCT) is selected as sparse dictionary matrix to represent the sparse basis while Basis Pursuit is selected as sparse reconstruction algorithm to reconstruct the sparse signal from measurement data. The measured $S_{21}$ data are successfully reconstructed into an image using the Compressive Sensing approach. The reconstructed image is analyzed both qualitatively and quantitatively using image quality parameters such as Structural Similarity Index (SSIM) and Mean Squared Error (MSE).

Accurate distance information is essential for remote sensing mapping and 3D information reconstruction. Dense depth reconstruction from sparse measurements in indoor and outdoor natural scenes has been extensively researched. However, in aerial scenes, dense height image reconstruction from sparse height measurements has received relatively limited attention. This paper investigates the challenge of reconstructing aerial ground height from sparse height measurements. In this paper, we comprehensively evaluate the feasibility and performance of various sparse reconstruction algorithms in aerial scenes for the first time, and explore the optimal sampling rate and pattern for preserving the image structure and details. Additionally, we propose two novel algorithms based on the normalized weighted non-local Laplacian, which can achieve state-of-the-art results without relying on color image guidance. Moreover, our algorithm is inherently robust to different sampling configurations. The solver tools relied upon also have proven hardware and software optimisation and acceleration schemes in the industry, making them well suited for deployment in real-world scenarios. We validate the effectiveness and robustness of the proposed algorithms on three datasets.

Active three-dimensional (3D) microwave and millimeter-wave imaging is useful for a variety of applications including concealed weapon detection, in-wall imaging, non-destructive evaluation, and others. High-resolution imaging is usually performed using a fixed two-dimensional planar or cylindrical aperture that is defined using a two-dimensional array or precise mechanical scanning of a transceiver or sequentially-switched linear antenna array. For some applications, it is more convenient to manually translate a linear array over the scene of interest, or equivalently, move the target in front of the linear array to scan an effective aperture. Manually scanning the array or target creates several challenges for accurately focusing, or reconstructing, an image of the target. The motion of the array or target must be known accurately, typically with precision of 0.05-0.1 wavelengths. Additionally, the image reconstruction algorithm needs to be able to compensate for aperture shapes which are highly non-uniformly sampled, and which are not of a specific canonical shape such as planar or cylindrical. This paper explores high-resolution 3D microwave imaging of a moving target by using optical motion capture to track the moving target and develops highly versatile image reconstruction techniques that account for the irregular motion. Several experimental results are shown for moving targets in front of a fixed linear array.

Thermal Imaging can be really valuable to military, other users of surveillance camera and physicians. Contrary to the usual sampling process where weavers want to sample at the Nyquist rate the Compressive Sensing provides a stricter sampling condition when the signal is known to be sparse or compressible. It turned out that the Nyquist-Shannon theorem is a sufficient condition but not a necessary condition for perfect reconstruction. The compressive sensing field of interest provides a stricter sampling condition when the signal is known to be sparse or compressible. This theorem still struggles with three main problems: sparse representation, measurement matrix and reconstruction algorithm. Unfortunately, thermal image camera resolution is considerably lower than of optical cameras, mostly only 160×120 or 320×240 pixels narrow view. The 360-degree infrared surveillance would be the clear choice. Day or night, the IR Revolution 360 panoramic infrared camera provides instantaneous, long range, wide area, high resolution thermal imaging. The method utilizes double sensor thermal imaging cameras. Moreover the temporal resolution has been enhanced. This paper goal is to keep evolving with compressed sensing and thermal imaging technology.

A new compressed sensing (CS) image reconstruction method is proposed for high‐resolution wideband three‐dimensional synthetic aperture radar (SAR) imaging systems. In contrast to existing CS SAR methods that employ only a forward SAR transform in pre‐ or post‐processing, the proposed method employs both forward SAR and reverse SAR (R‐SAR) transforms in each CS iteration to improve the quality of reconstructed images. This study proposes a simple and elegant truncation repair method to combat the truncation error and utilises non‐uniform fast Fourier transform to reduce the SAR and R‐SAR transform errors, thereby ensuring the convergence of the CS algorithm and improving the quality of the reconstructed images. The proposed CS SAR method is applied to microwave and millimeter wave imaging systems for non‐destructive evaluation of materials embedded in stratified media. Three different specimens under test are measured by conventional uniform sampling and by random under‐sampling with 20% or 30% spatial points of the uniform sampling. The reconstructed images show that, albeit having much less measurement points, the proposed CS method achieves better image quality and lower background artefacts than the images reconstructed from the fully‐sampled uniform measurements.

A review of the most recent contributions in the framework of Compressive Sensing (CS) strategies as applied to Electromagnetics is presented in this work. Towards this end, the CS fundamentals are firstly discussed in the light of their impact in Electromagnetics technologies. A set of CS applicative scenarios is then discussed, taking into account problems coming from antenna array design and diagnosis, direction-of-arrival estimation, and microwave imaging. An assessment of the effectiveness and potentialities of CS techniques in Electromagnetics is provided by selected numerical examples.

Microwave imaging (MWI) has emerged as a promising non-invasive diagnostic technology in medical applications, offering significant advantages over conventional imaging modalities including reduced radiation exposure, cost-effectiveness, and enhanced patient comfort. This comprehensive survey presents a systematic review of MWI through a novel bifurcated taxonomy addressing both methodological aspects (imaging techniques, deep learning approaches, and antenna design) and application domains (breast imaging, brain imaging, and stroke diagnostics). The methodological analysis reveals substantial progress in reconstruction algorithms, with physics-assisted deep learning frameworks demonstrating superior performance in automated thresholding and bias reduction compared to traditional qualitative imaging methods. Advanced techniques like MDLI-Net and DL-MITAT have shown particular promise in handling sparse data scenarios. In antenna technologies, ultra-wideband antennas and miniaturized millimeter-wave sensors represent significant breakthroughs, with some systems capable of detecting malignancies as small as 1mm. Application-wise, brain imaging applications have achieved remarkable accuracy rates exceeding 94% using YOLOv5 models, while breast imaging systems demonstrate effective tumor detection in dense tissues where conventional mammography faces limitations. Despite these advancements in computational methods, particularly deep learning and sophisticated reconstruction algorithms, several challenges remain in translating MWI technology from experimental settings to widespread clinical adoption. Our contributions include structured analysis of recent advances in MWI techniques, critical insights into challenges across medical applications, and identification of research gaps and future directions. Key findings demonstrate that combining qualitative imaging with deep neural networks achieves reliable real-time reconstruction capabilities. While MWI shows considerable potential as a safe, cost-effective alternative to traditional imaging techniques, particularly for breast and brain imaging, further research in computational efficiency, clinical validation, and standardization protocols is necessary for successful clinical implementation across diverse healthcare settings.

Deterministic construction of sparse binary matrices via incremental integer optimization

Sparse signals can be reconstructed from a small set of measurements basing on the theory of compressive sensing (CS), whereas the key points are the selection of the measurement matrix and the reconstruction algorithm. This paper presents an imaging algorithm for ground penetrating radar based on CS. The measurement matrix is selected via random filters, which can reduce the number of nonzero elements in the measurement matrix effectively. We adopt the simple orthogonal matching pursuit (OMP) algorithm to reconstruct signal with less data storage and lower computational complexity. Simulation results are provided to illustrate the performance of the proposed method.

Compressive sensing (CS) is a new technique that give an approach to reconstruct the signal with few numbers of observations or measurements. The CS is based on L1 -norm minimizations to find the sparse solutions and it is known as basis pursuit. In this investigation, CS scheme with different transforms is proposed. The three utilized transform techniques are: Discrete Fourier Transform(DFT), Discrete Cosine Transform(DCT) and Discrete Wavelet Transform(DWT) with daubechies1(DB1) and coiflets1(coif1) basis. The proposed system is tested by employing the following signals: Blocks, Heavy Sine, ‘Bumps’ and ‘Doppler’ which cover wide range of applications. The four testing signals are represented in sparse domain using different transforms. In order to threshold the coefficients of the signals in sparse domain, the universal threshold is utilized in the case of CS with FFT and DWT whereas, the universal threshold is modified to prune the DCT coefficients. The main aim of this study is to investigate the differences among CS with DFT, CS with DCT and CS with DWT, and consequently a suitable transform domain used with CS to be selected. The comparative study is established by assessing the performance of the proposed system using Root Mean Square Error (RMSE), output SNR, and the time required to reconstruct approximated signals. Simulation results have shown that the CS with DWT outperforms the CS with FFT and DCT. CS with DWT has achieved good RMSE values about (0.0014 to 3.359e-8) even when half of the signal elements are removed. CS with FFT and DCT enhanced the noisy Blocks and Bumps signals by 3dB and 1dB respectively, while it is failed to enhance noisy Heavy Sine and Doppler signals. CS with DWT of two basis and for single decomposition level have improved the noisy Blocks, Bumps, Heavy Sine and Doppler signals by 5dB, 4dB, 3dB, and 3dB respectively.

This paper devises a novel adaptive framework for the energy-aware acquisition of spectrally sparse signals. The adaptive quantized compressive sensing (CS) techniques, beyond-complementary metal–oxide–semiconductor (CMOS) hardware architecture, and corresponding algorithms which utilize them have been designed concomitantly to minimize the overall cost of signal acquisition. First, a spin-based adaptive intermittent quantizer (AIQ) is developed to facilitate the realization of the adaptive sampling technique. Next, a framework for smart and adaptive determination of the sampling rate and quantization resolution based on the instantaneous signal and hardware constraints is introduced. Finally, signal reconstruction algorithms which process the quantized CS samples are investigated. Simulation results indicate that an AIQ architecture using a spin-based quantizer incurs only 20.98- $\mu \text{W}$ power dissipation on average using 22-nm technology for 1–8 bits uniform output. Furthermore, in order to provide 8-bit quantization resolution, 85.302- $\mu \text{W}$ maximum power dissipation is attained. Our results indicate that the proposed AIQ design provides up to 6.18-mW power savings on average compared to other adaptive rate and resolution CMOS-based CS analog-to-digital converter designs. In addition, the mean square error values achieved by the simulation results confirm efficient reconstruction of the signal based on the proposed approach.

Compressed Sensing (CS) theory has gained so much attention recently in the areas of signal processing. The sparsity of the transform coefficients has been widely employed in the early CS recovery techniques. However, except for the sparsity, there are other priors about transform coefficients such as the tree structure and the statistical dependencies that could be employed in CS reconstruction. In this paper, we propose to introduce the Gaussian Scale Mixtures (GSM) model into the tree structure based Orthogonal Matching Pursuit (TSOMP) reconstruction algorithm. This GSM model can efficiently denote the statistical dependencies between wavelet coefficients. And these statistical dependencies will improve the accuracy of the searching of the tree structure subspace in TSOMP algorithm. When both the inter-scale dependences (such as GSM model) of the coefficients and the intra-scale dependences (such as tree structure) of the coefficients are combined into the Orthogonal Matching Pursuit reconstruction algorithm, the noise and instability in TSOMP reconstruction are well reduced. Some state-of-the-art methods are compared with the proposed method. Experimental results show that the proposed method improves reconstruction accuracy for a given number of measurements and more image details are recovered.

Electrical capacitance volume tomography is a volumetric tomography technique that utilizes capacitance and fringing to capture behavior or perturbation in the sensing domain. One of the crucial issues in developing ECVT technology is the reconstruction algorithm. In practice, ILBP is most used due to its simplicity. However, it still presents elongation errors for certain dielectric contrasts. The high undersampling measurement of the ECVT imaging system, which is mathematically defined as an undetermined linear system, is one of the most challenging issues. Compressive sensing (CS) is a framework that enables the recovery of a sparse signal or a signal that can be represented as sparse in a certain domain, by having a lower dimension of measurement data compared to the Shanon-Nyquist theorem. Thus, mathematically, this framework is promising for solving an undetermined linear system such as the ECVT imaging system. This paper discusses the possibility of developing an ECVT imaging technique for static objects based on a CS framework. Based on the simulation results, Non-optimized CS does not completely succeed in providing better ECVT imaging quality. However, it does provide more localized imaging compared to ILBP. In addition, by having fewer requirements for the measurement data dimension, the CS framework is promising for reducing the number of required electrodes.

An Adaptive Measurement Scheme (AMS) is investigated with Compressed Sensing (CS) theory in Cognitive Wireless Sensor Network (C-WSN). Local sensing information is collected via energy detection with Analog-to-Information Converter (AIC) at massive cognitive sensors, and sparse representation is considered with the exploration of spatial temporal correlation structure of detected signals. Adaptive measurement matrix is designed in AMS, which is based on maximum energy subset selection. Energy subset is calculated with sparse transformation of sensing information, and maximum energy subset is selected as the row vector of adaptive measurement matrix. In addition, the measurement matrix is constructed by orthogonalization of those selected row vectors, which also satisfies the Restricted Isometry Property (RIP) in CS theory. Orthogonal Matching Pursuit (OMP) reconstruction algorithm is implemented at sink node to recover original information. Simulation results are performed with the comparison of Random Measurement Scheme (RMS). It is revealed that, signal reconstruction effect based on AMS is superior to conventional RMS Gaussian measurement. Moreover, AMS has better detection performance than RMS at lower compression rate region, and it is suitable for large-scale C-WSN wideband spectrum sensing.

Compressed Sensing (CS) theory is a new method of signal acquisition and processing proposed in recent years. With small amount of sampling data recovering original data to precisely reconstruct sparse signal or compression signal, the theory breaks though the restriction of Nyquist sampling theorem. CS can avoid enormous sampling data waste but also reduce the complexity of image coding. This paper reviews the basic theory of CS and its three key points, including signal sparse representation, design of measurement matrix and reconstruction algorithms. Then, the application of CS in the field of remote sensing image compression technology is studied. Using MATLAB software, we do a series of CS emulation experiments compared with the traditional compression methods. The results show that the proposed method has a good performance on the remote sensing image compression.