期刊文献+

基于Tetrolet变换的红外与可见光融合 被引量:14

Infrared and Visible Images Fusion Based on Tetrolet Transform
下载PDF
导出
摘要 针对目前红外与可见光图像融合速度慢、融合结果对比度不高且易产生伪影的缺点,提出一种基于Tetrolet变换的改进融合算法。首先,将可见光图像转换到lαβ颜色空间得到三个几乎不相关的彩色通道;然后对其l分量和红外图像分别进行Tetrolet变换,对于低通系数引入邻域能量及其接近度的融合规则。而对Tetrolet系数采用伪随机傅里叶矩阵进行观测并加权融合其观测值;接下来对融合后观测值采用CoSaMP优化算法迭代出融合后的Tetrolet系数,并经Tetrolet重构得到融合后的灰度图像;最后将灰度图像映射到RGB颜色空间获得最终的融合图像。实验证明了本文算法的有效性。 The present study an improved fusion algorithm was proposed based on the Tetrolet transform. It was used to solve the problems that the infrared and visible light images fusion speed is slow, the contrast of the fused image is low and it is easy to bring artifacts to the fused image. First of all, the visible light image was converted to the lαβ color space to get three irrele- vant color channels. Secondly, the component l and infrared image were decomposed by the Tetrolet transform. The neighbor- hood energy and proximity were introduced to the low-pass coefficients fusion rule. The Tetrolet coefficients were observed by the pseudo-random Fourier matrix The observation value was weightedly fused. Thirdly, the fused observation value were iterated by the CoSaMP optimization algorithm to get the fused Tetrolet coefficient. The fused gray image was got after the Tetrolet reconstruction. Finally, the final fused image was obtained by mapping the grey image to the RGB color space. The experiment results testified the algorithm validity for the image fusion.
出处 《光谱学与光谱分析》 SCIE EI CAS CSCD 北大核心 2013年第6期1506-1511,共6页 Spectroscopy and Spectral Analysis
基金 国家自然科学基金项目(60962004 61162016) 甘肃省科技攻关计划基金项目(0708GKCA047) 甘肃省青年科技基金计划项目(1107RJYA017) 兰州交通大学青年基金项目(2012003)资助
关键词 红外图像 可见光图像 图像融合 Tetrolet变换 伪随机傅里叶矩阵 CoSaMP Infrared image Visible light image Image fusion Tetrolet transform Pseudo random Fourier matrix~ CoSaMP
  • 引文网络
  • 相关文献

参考文献2

共引文献3

同被引文献372

  • 1张闯,柏连发,张毅.基于灰度空间相关性的双谱微光图像融合方法[J].物理学报,2007,56(6):3227-3233. 被引量:8
  • 2冈萨雷斯.数字图象处理[M].北京:电子工业出版社,2003..
  • 3阮秋琦.数字图像处理学[M].北京:电子工业出版社,2007.
  • 4陆欢,吴庆宪,姜长生.基于PCA与小波变换的彩色图像融合算法[J].计算机仿真,2007,24(9):202-205. 被引量:13
  • 5Plonka G.The easy path wavelet transform:a new adaptive wavelet transform for sparse representation of two dimensional data[J].Multiscale Modeling and Simulation,2009,7(3):1474-1496.
  • 6Candes E J,Donoho D L.New tight frames of Curvelets and optimal representations of objects with piecewise C2 singularities[J].Communications on Pure and Applied Mathematics,2004,57(2):219-266.
  • 7Do M N,Vetterli M.The Contourlet transform:an efficient directional multi resolution image representation[J].IEEE Transactions on Image Processing,2005,14(12):2091-2106.
  • 8K Guo,Labate D.Optimally sparse multidimensional representation using shearlets[J].SIAM J Math,2007,39(1):298-318.
  • 9Velisavljevic V,Beferull-Lozano B,Vetterli M.Directionlets:anisotropic multidirectional representation with separable filtering[J].IEEE Transactions on Image Processing,2006,15(7):1916-1933.
  • 10Krommweh J.Tetrolet transform:a new adaptive Haar wavelet algorithm for sparse image representation[J].Journal of Visual Communication and Image Representation,2010,21(4):364-374.

引证文献14

二级引证文献76

;
使用帮助 返回顶部