Chen, Yang; Sun, Yiwen; Pickwell-MacPherson, Emma Total variation deconvolution for terahertz pulsed imaging. (English) Zbl 1219.78057 Inverse Probl. Sci. Eng. 19, No. 2, 223-232 (2011). Summary: In terahertz pulsed imaging (TPI), a deconvolution process is usually applied as an inverse operation to extract the sample impulse function for further imaging or spectroscopic analysis. Often, such deconvolution is achieved by direct inverse filtering (IF) or IF with a coupled low-pass or double Gaussian filter. However, the low-pass or double Gaussian filter cannot cope well with both suppressing noise and retrieving localized terahertz pulses and they often result in over-smoothing. Here, we apply the iterative total variation method to the deconvolution process in TPI with a view to enhance the sample impulse function and generate better terahertz images. Experiments with both simulated and raw data validate a better performance for the proposed approach. MSC: 78A40 Waves and radiation in optics and electromagnetic theory 65T50 Numerical methods for discrete and fast Fourier transforms 60G35 Signal detection and filtering (aspects of stochastic processes) 42A85 Convolution, factorization for one variable harmonic analysis Keywords:terahertz pulsed imaging (TPI); inverse filtering; double Gaussian filter; total variation (TV) deconvolution PDFBibTeX XMLCite \textit{Y. Chen} et al., Inverse Probl. Sci. Eng. 19, No. 2, 223--232 (2011; Zbl 1219.78057) Full Text: DOI References: [1] DOI: 10.1109/22.744288 · doi:10.1109/22.744288 [2] DOI: 10.1007/s003400050750 · doi:10.1007/s003400050750 [3] DOI: 10.1364/OL.14.001128 · doi:10.1364/OL.14.001128 [4] DOI: 10.1109/2944.571768 · doi:10.1109/2944.571768 [5] DOI: 10.1088/0031-9155/49/9/001 · doi:10.1088/0031-9155/49/9/001 [6] DOI: 10.1016/0167-2789(92)90242-F · Zbl 0780.49028 · doi:10.1016/0167-2789(92)90242-F [7] DOI: 10.1023/B:JMIV.0000011320.81911.38 · Zbl 1366.94051 · doi:10.1023/B:JMIV.0000011320.81911.38 [8] DOI: 10.1109/TIP.2007.909318 · Zbl 05516504 · doi:10.1109/TIP.2007.909318 [9] DOI: 10.1088/0031-9155/46/3/318 · doi:10.1088/0031-9155/46/3/318 [10] DOI: 10.1016/j.compmedimag.2008.12.007 · doi:10.1016/j.compmedimag.2008.12.007 [11] DOI: 10.1016/j.compmedimag.2009.06.005 · doi:10.1016/j.compmedimag.2009.06.005 [12] Zhang X, Constrained Total Variation Minimization and Application in Computerized Tomography 3757 (2005) [13] S. Ma, W. Yin, Y. Zhang, and A. Chakraborty,An efficient algorithm for compressed MR imaging using total variation and wavelets, inIEEE Conference on Computer Vision and Pattern Recognition (CVPR), 23–28 June 2008, pp. 1–8 [14] DOI: 10.1364/JOSAA.18.001562 · doi:10.1364/JOSAA.18.001562 [15] DOI: 10.1364/OE.15.004335 · doi:10.1364/OE.15.004335 [16] DOI: 10.1063/1.2989126 · doi:10.1063/1.2989126 [17] DOI: 10.1088/0031-9155/47/21/325 · doi:10.1088/0031-9155/47/21/325 [18] DOI: 10.1002/cphc.200700261 · doi:10.1002/cphc.200700261 This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.