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Volume 32 Issue 7
Aug.  2010
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Cheng Ping, Zhao Jia-qun, Si Xi-cai, Zhao Xin. L-R Imaging Algorithm for Passive Millimeter Wave Based on Sparse Representation[J]. Journal of Electronics & Information Technology, 2010, 32(7): 1707-1711. doi: 10.3724/SP.J.1146.2009.01017
Citation: Cheng Ping, Zhao Jia-qun, Si Xi-cai, Zhao Xin. L-R Imaging Algorithm for Passive Millimeter Wave Based on Sparse Representation[J]. Journal of Electronics & Information Technology, 2010, 32(7): 1707-1711. doi: 10.3724/SP.J.1146.2009.01017

L-R Imaging Algorithm for Passive Millimeter Wave Based on Sparse Representation

doi: 10.3724/SP.J.1146.2009.01017
  • Received Date: 2009-07-17
  • Rev Recd Date: 2009-12-01
  • Publish Date: 2010-07-19
  • In passive millimeter wave image restoration, L-R algorithm is a simple and effective nonlinear method. However, when the noise can not be neglected, it is difficult for L-R algorithm to get good restoration. As a novel signal processing method, adaptive sparse representation has a merit of representing signal flexibly and can de-noise effectively when maintaining features of targets. A novel L-R algorithm is proposed based on adaptive sparse representation. It first de-noises by employing sparse signal representation, and then restores images by using L-R algorithm. The modified algorithm reduces the influence of noise on L-R algorithm effectively by using de-noise algorithm based on adaptive sparse representation. The imaging results of experiment data show that the modified algorithm proposed in the paper improves the performance of L-R algorithm, and it can be used in image restoration when the signal to noise ratio is low.
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  • Pirogov Y A, Gladun V V, and Tischenko D A, et al.. Passive millimeter-wave imaging with superresolution[C]Image and Signal Processing for Remote Sensing X, Proc. of SPIE, Bellingham, WA, 2004, 5573: 72-83. [2] Lettington A H, Yallop M R, and Dunn D. Review of super-resolution techniques for passive millimeter-wave imaging[C]. Infrared and Passive Millimeter-wave Systems: Design, Analysis, Modeling, and Testing, Proc. of SPIE, Bellingham, USA, 2002, 4719: 230-239. [3] Dupe F X, Fadili J M, and Starck J L. A proximal iteration for deconvolving Poisson noisy images using sparse representations[J].. IEEE Transactions on Image Processing.2009, 18(2):310-321
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