投影小波域MAP估计无源毫米波图像超分辨算法

金鑫; 熊金涛; 李良超; 杨建宇

doi:10.3724/SP.J.1146.2009.00547

投影小波域MAP估计无源毫米波图像超分辨算法

doi: 10.3724/SP.J.1146.2009.00547

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出版历程
- 收稿日期: 2009-04-13
- 修回日期: 2009-11-23
- 刊出日期: 2010-04-19

Projected Wavelet-Domain MAP Estimation Super-resolution Algorithm for Passive Millimeter Wave Imaging

摘要

摘要: 在无源毫米波成像中，由于系统天线孔径大小的受限而使得成像的分辨率低。为了提高图像的分辨率，该文提出了一种投影小波域最大后验(MAP)估计毫米波图像超分辨算法(PWMAP)。该算法利用基于小波域广义高斯分布的MAP估计来恢复通带内的频谱；然后利用投影的非线性运算实现频谱外推。该算法不仅比以往的算法能提供更准确的先验建模，而且能在每步迭代时自适应地更新正则参数。实验结果验证了该算法的有效性。
- 无源毫米波成像; 超分辨; 小波域; 自适应; 非线性运算
Abstract: In passive millimeter wave imaging, the problem of poor resolution of acquired image stems mainly from system antenna size limitations. In order to achieve resolution improvements, a Projected Wavelet-domain Maximum A Posteriori (PWMAP) estimation super-resolution algorithm is proposed in this paper. This algorithm restores the spectrum in the pass-band based on wavelet domain using the generalized Gaussian distribution and the MAP estimate; then extrapolate the spectrum by using the non-linear projection operation. This algorithm can not only provide a more accurate priori model than previous algorithms, but also updates the parameter adaptively at each iteration. Experimental results show the effectiveness and superiority of the algorithm.

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Lettington A H, Yallop M R, and Dunn D. Review ofsuper-resolution techniques for passive millimeter-waveimaging[J]. Proceeding of SPIE, 2002, 4719: 203-239.[2]Ogawa Takahiro and Haseyama Miki. Adaptivereconstruction method of missing texture based on projectiononto convex sets [C]. ICASSP, IEEE InternationalConference on Acoustics, Speech and Signal Processing,Hawaii, America, 2007. Vol.1: 1697-1700.[3]Zhao Zong-qing, Ding Yong-kun, and Dong Jian-jun, et al..Richardson-Lucy method for decoding x-ray ring codeimage[J].Plasma Physics and Controlled Fusion.2007, 49(8):1145-1150[4]Hunt B R and Sementilli P. Description of a Poisson imagerysuper resolution algorithm[C]. Astronomical Data AnalysisSoftware and Systems,San Francisco, America, 1992. Vol.25:196-199.[5]Zheng Xin, Yang Ji-Yu, and Li Liang-cao, et al.. Waveletbasedsuper-resolution algorithms for passive millimeter waveimaging[C]. 2008 International Conference onCommunication, Circuits and Systems(ICCCAS'08), XiaMen, China, May 2008, Vol.2: 927-930.[6]Mallat S. A theory for multiresolution signal decomposition:The wavelet representation [J].IEEE Transactions onPattern Analysis and Machine Intelligence.1989, 11(7):674-693[7]张新明, 沈兰荪. 在小波变换域内实现图像的超分辨率复原[J]. 计算机学报, 2003, 26(9): 1183-1189.Zhang Xin-ming and Shen Lan-sun. Super-resolutionrestoration from image sequences in the wavelet domain[J].Chinese Journal of Computers, 2003, 26(9): 1183-1189.[8]王正明, 朱炬波. SAR图像提高分辨率技术[M]. 北京: 科学出版社, 2006: 143-144.[9]Eicke B. Iteration methods for convexly constrained ill-posedproblems in Hilbert space [J].Numerical Functional Analysisand Optimization.1992, 13(56):413-429[10]Vonesch C and Unser M. A fast thresholded landweberalgorithm for wavelet-regularized multidimensionaldeconvolution [J].IEEE Transactions on Image Processing.2008, 17(4):539-549[11]汪太月, 李志明. 一种广义高斯分布的参数快速估计法[J]. 工程地球物理学报, 2006, 3(3): 172-176.Wang Tai-yue and Li Zhi-ming. A fast parameter estimationof generalized Gaussian distribution [J]. Chinese Journal ofEngineering Geophysics, 2006, 3(3): 172-176.[12]Zheng Xin and Yang Jian-yu. Adaptive projected Landwebersuper-resolution algorithm for passive millimeter waveimaging[C]. 2007 SPIE Fifth International Symposium onMultispectral Image Processing and Pattern Recognition.Vol.6787: 67871k-1-67871k-7.

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