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Full-Text Articles in Computer Engineering
Modified Sparse Quasi - Newton Algorithm For Electrical Capacitance Tomography System, Chen Yu, Zongji Xia, Yujia Zhou
Modified Sparse Quasi - Newton Algorithm For Electrical Capacitance Tomography System, Chen Yu, Zongji Xia, Yujia Zhou
Journal of System Simulation
Abstract: To solve the 'soft-field' nature and the ill-posed problem in electrical capacitance tomography technology, a modified sparse quasi - newton algorithm for electrical capacitance tomography is presented. The mathematical model of modified sparse quasi - newton is derived. The final iteration formula of modified sparse quasi - newton used to adjust the inverse problem solving of the capacitance tomography image reconstruction is given. An iterative formula for ECT inverse problem solving is used for digital simulation experiment. The simulation experiment results are compared with the results of LBP, Landweber, CG, SD, and so on. The results of the analysis …
Sparsity-Based Three-Dimensional Image Reconstruction For Near-Field Mimo Radar Imaging, Fi̇gen S. Oktem
Sparsity-Based Three-Dimensional Image Reconstruction For Near-Field Mimo Radar Imaging, Fi̇gen S. Oktem
Turkish Journal of Electrical Engineering and Computer Sciences
Near-field multiple-input multiple-output (MIMO) radar imaging systems are of interest in diverse fields such as medicine, through-wall imaging, airport security, concealed weapon detection, and surveillance. The successful operation of these radar imaging systems highly depends on the quality of the images reconstructed from radar data. Since the underlying scenes can be typically represented sparsely in some transform domain, sparsity priors can effectively regularize the image formation problem and hence enable high-quality reconstructions. In this paper, we develop an efficient three-dimensional image reconstruction method that exploits sparsity in near-field MIMO radar imaging. Sparsity is enforced using total variation regularization, and the …
No-Reference Image Denoising Quality Assessment, Si Lu
No-Reference Image Denoising Quality Assessment, Si Lu
Computer Science Faculty Publications and Presentations
A wide variety of image denoising methods are available now. However, the performance of a denoising algorithm often depends on individual input noisy images as well as its parameter setting. In this paper, we present a noreference image denoising quality assessment method that can be used to select for an input noisy image the right denoising algorithm with the optimal parameter setting. This is a challenging task as no ground truth is available. This paper presents a data-driven approach to learn to predict image denoising quality. Our method is based on the observation that while individual existing quality metrics and …