期刊文献+

基于快速非抽样小波变换的岩屑多聚焦图像融合

Multi-focus debris image fusion based on fast non-sampling wavelet transform
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摘要 针对传统非抽样小波变换算法较复杂的缺点,结合空、频域处理上的特点,提出了一种基于快速非抽样小波变换的多聚焦图像融合算法。与之前基于非抽样小波变换的融合算法不同,该算法取消了反变换,它根据高频小波系数绝对值和取大原则,融合图像像素值直接在对应源图像的相应位置取值,从而大大提高了图像处理的实时性,改善了融合效果。通过与六种非抽样小波变换融合算法的比较,以及快速非抽样小波变换与非抽样小波变换的融合时间对比,直观地给出了该算法的效果和时间优势。 For the computational complexity of traditional non-sampling wavelet algorithm, a method of multi-focus image fusion based on fast non-sampling wavelet with the advantage of the characteristic of the processing on both space and frequency domain is proposed in this paper. Inverse transform is not needed as compared to previous non-sampling wavelet algorithms. Pixel value of fusion image is directly taken at the corresponding position of the source image based on the maximum principle of the sum of the absolute value of high frequency wavelet coefficients. Hence, there is a great improvement both on the real-time requirement of image processing and the fusion effect. Two comparisons, namely, comparison between the algorithm and six tra- ditional non-sampling wavelet algorithms, comparison between fast non-sampling wavelet transform and traditional sampling wavelet transform are made in the work. The comparison results validate the advantage of the algorithm on effect and the time cost.
出处 《计算机工程与应用》 CSCD 2013年第11期195-198,共4页 Computer Engineering and Applications
基金 国家自然科学基金(No.60972130) 四川省科技支撑计划资助项目(No.2010GZ0167)
关键词 快速非抽样 小波变换 融合算法 空域 fast non-sampling wavelet transform fusion algorithm space domain
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参考文献8

  • 1Zhang Qiang, Guo Bao-long.Multifocus image fusion using the nonsubsampled contourlet transform[J].Signal Processing, 2009,89(7) : 1334-1346.
  • 2Yang Shu-yuan, Wang Min, Lu Yan-xiong, et al.Fusion of multiparametric SAR images based on SW-nonsubsampled contourlet and PCNN[J].Signal Processing, 2009, 89(12): 2596-2608.
  • 3Balster E J, Zheng Y F, Ewing R L.Feature-based wavelet shrinkage algorithm for image denoising[J].IEEE Trans on Image Processing, 2005,14 (12) : 2024-2039.
  • 4矫媛,黄斌文,羊秀青.基于上下文模型的非抽样小波图像去噪[J].科技信息,2010(19). 被引量:1
  • 5Kie B.Fusion of multiple images with robust random field models[J].IEEE, 2003 : 335-338.
  • 6Nunezetal J, Otazu X, Fors O.Multiresolution-based image fusion with additive wavelet decomposition[J].IEEE Transac- tions on Geoscience and Remote Sensing, 1999,37(3): 1204-1211.
  • 7靳士利,赵志刚.基于非抽样小波的多阈值去噪[J].青岛大学学报(自然科学版),2009,22(4):77-81. 被引量:2
  • 8李光鑫,王珂,张立保.加权多分辨率图像融合的快速算法[J].中国图象图形学报,2005,10(12):1529-1536. 被引量:13

二级参考文献32

  • 1段瑞玲,李玉和,李庆祥,贾惠波.非线性阈值自调整小波图像去噪方法研究[J].光电子.激光,2006,17(7):871-874. 被引量:20
  • 2Donoho D L, Johnstone I M. Ideal Spatial Adaptation via Wavelet Shrinkage [J]. Biometrika, 1994, 81(3): 425- 435.
  • 3Chang S G, Yu Bin, Martin Vetterli. Adaptive Wavelet Thresholding for Image Denoising and Compression [J]. IEEE Transaction On Image Processing, 2000, 9(9): 1532-1546.
  • 4Gnanadurai D, Sadasivam V, Muthukumaran Let al. Undecimated double density wavelet transform based speckle reduction in SAR images [J]. Computers and Electrical Engineering, 2009(35) : 209 - 217.
  • 5Bao P, Zhang L. Noise reduction for magnetic resonance images via adaptive multiscale products thresholding [J]. IEEE Trans. Medical Imaging, 2003, 22(9): 1089-1099.
  • 6Donoho D L. Denoising by soft thresholding [J]. IEEE Transaction On Information Theory, 1995, 41(3): 613 -627.
  • 7Steve De Backer, Aleksandra Pizurica, et al. Denoising of multicomponent image using waveIet least squares estimators[J]. Image and Vision Compute, 2008(26): 1038-1051.
  • 8Cai T T, Silverman B W. Incorporating information on neighbouring coefficients into wavelet estimation [J]. Sankhya, 2001, 63(2): 127-148.
  • 9Oliver Schall, Alexander Belyaev, Hans-Peter Seidel. Adaptive feature-preserving non-local denoising of static and timevarying range data [J]. Computer-Aided Design, 2008(40) : 701 - 707.
  • 10Mallat.A theory for muhi-resolution decomposition:the wavelet shrinkage[J].Biometrika,1994,81:425-452.

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