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基于非下采样Contourlet变换多聚焦图像融合 被引量:2

Fusion of the Multifocus Images Based on the Nonsubsampled Contourlet Transform
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摘要 针对同一场景多聚焦图像的融合问题,本文提出了一种基于非下采样Contourlet变换(NSCT)多聚焦图像融合算法。首先,采用NSCT对源图像进行多尺度、多方向分解,得到低频子带系数和各带通方向子带系数;其后,针对低频子带系数的选择,提出了一种基于方向向量模和加权平均相结合的融合规则;然后,针对带通方向子带系数的选择,提出了一种基于改进的方向对比度和局部区域能量相结合的融合规则;最后,经NSCT逆变换得到融合图像。实验结果表明,该算法能够有效地保留源图像的有用信息,避免噪声、虚影等效应,是一种有效可行的图像融合算法。 For the fusion problem of the multifocus images with the same scene, a novel multifocus image fusion algo rithm based on the nonsubsampled contourlet transform (NSCT) is proposed. Firstly, source images are decomposed at dif- ferent scales and directions by NSCT, thus the low frequency subband coefficients and various bandpass directional subband coefficients are obtained. Secondly, for the low frequency subband coefficients, we present a fusion rule based on the direc tional vector normal combined with the weighted average; while for the bandpass directional subband coefficients, we pres ent a fusion rule based on the improved directional contrast combined with the local area energy. Finally, the fusion image is obtained through the inverse NSCT. The experimental results illustrate that the proposed algorithm is effective for retaining the original images~ information and avoiding artifacts.
出处 《计算机工程与科学》 CSCD 北大核心 2010年第11期71-74,共4页 Computer Engineering & Science
基金 江苏省自然科学基金资助项目(BK20080544)
关键词 图像融合 非下采样CONTOURLET变换 融合规则 方向向量模 方向对比度 image fusion nonsubsampted Contourlet transform fusion rule directional vector normal directional contrast
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  • 1徐华楠,刘哲,胡钢.Contourlet变换及其在图像去噪中的应用研究[J].计算机应用研究,2009,26(2):401-405. 被引量:15
  • 2Do M N, Vetterli M. The contourlet transform : an efficient di- rectional multiresolution image representation [ J ]. IEEE Transactions on Image Processing, 2005, 14 ( 12 ) : 2091 - 2106.
  • 3Cunha D A L,Zhou J, Do M N. The nonsubsampled contourlet transform : theory, design, and applications [ J ]. IEEE Transac- tions on Image Processing,2006,15(lO):3089-3101.
  • 4Chang S G, =J: Yu B, Vetterli M. Spatially adaptive wavelet thresholding with context modeling for image denoising [ J ]. IEEE Trans on Image Processing,2000,9 (9) : 1522-1531.
  • 5Donoho D L. De- noising by soft- thresholding [ J ]. IEEE Transactions on Information Theory, 1995,41 (3) :613-627.
  • 6Chang S G, Yu B, Vetterli M. Adaptive wavelet thresholding for image denoising and compression [ J ]. IEEE Transactions on Image Processing,2000,9 ( 9 ) : 1532-1546.
  • 7Po D D Y, Do M N. Directional multiscale modeling of images using the contourlet transform [ J ]. IEEE Transactions on Im- age Processing, 2006,15 ( 6 ) : 1610 - 1620.
  • 8Selvathi D, Malini C, Shanmugavalli P. Automatic segmentation and classification of liver tumor in CT images using adaptive hybrid technique and Contourlet based ELM classifier [ C ]/! Proc of 2013 international conference on recent trends in in- formation technology. [ s. 1. ] : IEEE ,2013:250-256.
  • 9杨晓慧,朱秀阁.基于非下采样Contourlets的CT/MRI图像自适应融合[J].计算机技术与发展,2008,18(12):116-119. 被引量:3
  • 10娄帅,丁振良,袁峰.基于小波域HMT模型融合的图像分辨率增强(英文)[J].北京交通大学学报,2008,32(6):106-110. 被引量:1

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