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
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
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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