- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Remote Sensing Image Compression in Visible/Near-Infrared Range Using Heterogeneous Compressive Sensing
摘要： Compressive sensing (CS) framework is very suitable for onboard image compression of high-resolution remote sensing cameras in the visible/near-infrared range (VI/NI-RSC) because it has the low-complexity in the sampling measurement stage. In this paper, we propose a new heterogeneous CS method for VI/NI-RSCs. Different from conventional CS methods evenly allocating sensing resources, the proposed method fully employs texture-feature information of remote sensing images to guide the allocation of sensing resources. More sensing resources are allocated to high-frequency regions, but fewer to low-frequency regions. The heterogeneous distribution of sensing resources obtains high reconstruction quality at the same compression performance, as well as high compression performance at the same level reconstructed quality. The shift of sensing resources is consistent with artificial image interpretations, i.e., human visual characteristics, where high-frequency regions, such as edges and textures, are the principal proof of the ground target identification. Experimental results indicate that the proposed method has better reconstruction quality than conventional CS method where texture-features are not utilized.
关键词： panchromatic images,remote sensing image compression,Heterogeneous compressive sensing (CS)
更新于2025-09-23 15:23:52
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Fabrication of 2D thin-film filter-array for compressive sensing spectroscopy
摘要： We demonstrate 2D filter-array compressive sensing spectroscopy based on thin-film technology and a compressive sensing reconstruction algorithm. To obtain different spectral modulations, we fabricate a set of multilayer filters using alternating low- and high-index materials and reconstruct the input spectrum using a small number of measurements. Experimental results show that the fabricated filter-array provides compatible spectral resolution performance with a conventional spectrometer in monochromatic lights and LEDs. In addition, the fabricated filter-array covers a wide range of wavelengths with a single exposure.
关键词： Compressive sensing,Thin films,Spectroscopy,Inverse problems
更新于2025-09-23 15:23:52
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Off-Grid Compressive Channel Estimation for mm-Wave Massive MIMO with Hybrid Precoding
摘要： To reduce the pilot overhead and improve the channel estimation accuracy in the massive MIMO system, various channel estimation algorithms employing the sparse signal reconstruction (SSR) scheme have been proposed. However, the spatial grid division leads to the trade-off between the estimation accuracy and the computational complexity. In addition, when the true angle is not on the discretized grid point which is referred as off-grid problem, the performance of SSR-based algorithms will degrade heavily. In this letter, a novel channel estimation algorithm which achieves superior performance under the off-grid scenario is proposed. At first, the conventional joint angle of arrivals/departures (AoAs/AoDs) estimation is transformed into two one-dimensional sub-problems. Then, the SSR-based framework is presented to obtain the initial sparse-support set. By minimizing the constructed objective function, the off-grid errors regarded as adjustable parameters are iteratively refined. In addition, scatter gains are acquired by LSE. Numerical simulations are provided to illustrate the superiority of the proposed algorithm in terms of estimation accuracy and computational complexity.
关键词： channel estimation,millimeter wave,massive MIMO,off-grid refinement,Compressive sensing
更新于2025-09-23 15:23:52
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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens, Greece (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - An Interior Point Method for Nonnegative Sparse Signal Reconstruction
摘要： We present a primal-dual interior point method (IPM) with a novel preconditioner to solve the (cid:96)1-norm regularized least square problem for nonnegative sparse signal reconstruction. IPM is a second-order method that uses both gradient and Hessian information to compute effective search directions and achieve super-linear convergence rates. It therefore requires many fewer iterations than first-order methods such as iterative shrinkage/thresholding algorithms (ISTA) that only achieve sub-linear convergence rates. However, each iteration of IPM is more expensive than in ISTA because it needs to evaluate an inverse of a Hessian matrix to compute the Newton direction. We propose to approximate each Hessian matrix by a diagonal matrix plus a rank-one matrix. This approximation matrix is easily invertible using the Sherman-Morrison formula, and is used as a novel preconditioner in a preconditioned conjugate gradient method to compute a truncated Newton direction. We demonstrate the efficiency of our algorithm in compressive 3D volumetric image reconstruction. Numerical experiments show favorable results of our method in comparison with previous interior point based and iterative shrinkage/thresholding based algorithms.
关键词： nonnegative sparse,3d volumetric image reconstruction,primal-dual preconditioned interior point method,(cid:96)1-norm regularized optimization,compressive sensing
更新于2025-09-23 15:22:29
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[IEEE 2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop (SAM) - Sheffield (2018.7.8-2018.7.11)] 2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop (SAM) - Super-Resolution Pulse-Doppler Radar Sensing via One-Bit Sampling
摘要： This paper investigates the delay-Doppler estimation problem of a pulse-Doppler radar which samples and quantizes the noisy echo signals to one-bit measurements. By applying a multichannel one-bit sampling scheme, we formulate the delay-Doppler estimation as a structured low-rank matrix recovery problem. Then the one-bit atomic norm soft-thresholding method is proposed to recover the low-rank matrix, in which a surrogate matrix is properly designed to evaluate the proximity of the recovered data to the sampled one. With the recovered low-rank matrix, the delays and Doppler frequencies can be determined and paired. Numerical experiments are performed to demonstrate the effectiveness of our method compared with the one-bit sparse signal recovery method based on discrete dictionary.
关键词： Delay-Doppler estimation,atomic norm,compressive sensing,1-bit quantization
更新于2025-09-23 15:22:29
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[IEEE 2018 5th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE) - Semarang (2018.9.27-2018.9.28)] 2018 5th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE) - Compressive Sensing Approach with Double Layer Soft Threshold for ECVT Static Imaging
摘要： Electrical Capacitance Volume Tomography (ECVT) is a capacitance based tomography technology which is developed since its advantages on non-invasive properties, low energy, and portability. One of the challenge on developing this tomography technology is on its imaging algorithm. Naturally the imaging method forms under-determined linear system which is indicated by dimension of the measurement is much smaller compared to the projected value dimension. Mathematically it implies ill-posed inverse problem. Therefore Compressive Sensing framework is used to solve the corresponding inverse problem. To improve the accuracy of the predicted image reconstruction, new threshold approach, Double Layer Soft Threshold, is proposed and attached to the proposed Compressive Sensing based ECVT imaging method. The simulations results show that the proposed method is able to improve the conventional ECVT imaging method, Iterative Linear Back Projection (ILBP), by significantly eliminating the elongation error.
关键词： Imaging method,Electrical Capacitance Volume Tomography,Compressive Sensing,Double Layer Soft Threshold
更新于2025-09-23 15:22:29
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Nonlocal Compressive Sensing-Based SAR Tomography
摘要： Tomographic synthetic aperture radar (TomoSAR) inversion of urban areas is an inherently sparse reconstruction problem and, hence, can be solved using compressive sensing (CS) algorithms. This paper proposes solutions for two notorious problems in this field. First, TomoSAR requires a high number of data sets, which makes the technique expensive. However, it can be shown that the number of acquisitions and the signal-to-noise ratio (SNR) can be traded off against each other, because it is asymptotically only the product of the number of acquisitions and SNR that determines the reconstruction quality. We propose to increase SNR by integrating nonlocal (NL) estimation into the inversion and show that a reasonable reconstruction of buildings from only seven interferograms is feasible. Second, CS-based inversion is computationally expensive and therefore, barely suitable for large-scale applications. We introduce a new fast and accurate algorithm for solving the NL L1-L2-minimization problem, central to CS-based reconstruction algorithms. The applicability of the algorithm is demonstrated using simulated data and TerraSAR-X high-resolution spotlight images over an area in Munich, Germany.
关键词： interferometric synthetic aperture radar (InSAR),tomographic SAR (TomoSAR),Compressive sensing (CS),nonlocal (NL) filtering
更新于2025-09-23 15:22:29
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Nonconvex Adaptive Weighted Regularization for Compressive Sensing Image Reconstruction Using Multiple-prespecified-dictionary Sparse Representation
摘要： Multiple-prespecified-dictionary sparse representation (MSR) has shown powerful potential in compressive sensing (CS) image reconstruction, which can exploit more sparse structure and prior knowledge of images for minimization. Due to the popular L1 regularization can only achieve the suboptimal solution of L0 regularization, using the nonconvex regularization can often obtain better results in CS reconstruction. This paper proposes a nonconvex adaptive weighted Lp regularization CS framework via MSR strategy. We first proposed a nonconvex MSR based Lp regularization model, then we propose two algorithms for minimizing the resulting nonconvex Lp optimization problem. According to the fact that the sparsity levels of each regularizers are varying with these prespecified-dictionaries, an adaptive scheme is proposed to weight each regularizer for optimization by exploiting the difference of sparsity levels as prior knowledge. Simulated results show that the proposed nonconvex framework can make a significant improvement in CS reconstruction than convex L1 regularization, and the proposed MSR strategy can also outperforms the traditional nonconvex Lp regularization methodology.
关键词： adaptive weighting,multiple-prespecified-dictionary,compressive sensing,nonconvex,sparse representation.
更新于2025-09-23 15:22:29
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[IEEE 2018 Second International Conference on Advances in Electronics, Computers and Communications (ICAECC) - Bangalore (2018.2.9-2018.2.10)] 2018 Second International Conference on Advances in Electronics, Computers and Communications (ICAECC) - Sparse Reconstruction of Hyperspectral Image using Bregman Iterations
摘要： Hyperspectral image processing plays an important role in satellite communication. Hyperspectral Image (HSI) processing requires very high ‘computational resources’ in terms of computational time and storage due to extremely large volumes of data collected by imaging spectrometers on-board the satellite. The bandwidth available to transmit the image data from satellite to the ground station is limited. As a result, Hyperspectral image compression is an active research area in the research community in past few years. The research work in the paper proposes a new scheme, Sparsification of HSI and reconstruction (SHSIR) for the reconstruction of hyperspectral image data acquired in Compressive sensing (CS) fashion. Compressed measurements similar to compressive sensing acquisition are generated using measurement matrices containing gaussian i.i.d entries. Now the reconstruction is solving the constrained optimization problem with non smooth terms. Adaptive Bregman iterations method of multipliers is used to convert the difficult optimization problem into a simple cyclic sequence problem. Experimental results from research work indicates that the proposed method performs better than the other existing techniques.
关键词： SHSIR algorithm,Hyperspectral image (HSI),Compressive sensing (CS)
更新于2025-09-23 15:22:29
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[IEEE 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Chongqing (2018.6.27-2018.6.29)] 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Compressive Hyperspectral Imaging and Super-resolution
摘要： Coded aperture snapshot spectral imager (CASSI) has been a popular spectral imaging architecture for its ability of capturing hyperspectral temporal resolution. However, such snapshot imaging system entails a large sacrifice in the spatial resolution of the data cube, since only a small amount of light gets into the imager during one snapshot. Also, the spatial resolution of the CASSI system is limited by the pixel size (and amount) of the detector, while it is difficult to fabricate a dense detector with small pixel size, especially for infrared spectral bands. Super-resolution is an advanced post-processing technique to alleviate such problem by exploiting the prior information of the image. In this letter, we try to realize image super-resolution from the perspective of developing new form of measurements by taking advantage of a modified CASSI system equipped with a coded aperture with higher spatial resolution than the detector, merging the SR model into the hardware configuration. Then the original data cube can be reconstructed from lower resolution measurements, thus the super-resolution is realized during the compressive sensing reconstruction process. The new system can be achieved based on the classical CASSI architecture in two dual ways, one by replacing the coded aperture with a higher resolution one and the other by substituting the focal plane array (FPA) detector with a lower resolution one. The experiments show that, we can recover images of higher quality with the first modification of CASSI system above, simply using a higher resolution coded aperture.
关键词： super resolution,compressive sensing,spectral imaging
更新于2025-09-23 15:22:29