基于平移不变剪切波变换域图像融合算法

刘卫; 殷明; 栾静; 郭宇

doi:doi:10.3788/gzxb20134204.0496

光子学报, 2013, 42 (4): 496, 网络出版: 2013-04-18

基于平移不变剪切波变换域图像融合算法

Image Fusion Algorithm Based on Shift-invariant Shearlet Transform

论文大纲

刘卫 ^*殷明栾静郭宇

作者单位

合肥工业大学数学学院,合肥 230009

图像融合平移不变剪切波变换奇异值分解 S函数 Image fusion Shift-invariant shearlet transform Singular value decomposition Sigmoid function

摘要

针对传统基于多尺度变换的图像融合方法存在的缺点, 提出了一种基于平移不变剪切波变换域的自适应图像融合新方法.首先, 使用平移不变剪切波变换对源图像进行分解, 得到低频子带及方向带通子带系数.然后, 对于低频子带系数采用梯度域奇异值分解方法估计图像的局部结构信息, 提出了基于提取的特征与S函数的可变加权融合策略; 对于各方向带通子带系数, 提出了一种基于改进的拉普拉斯能量和匹配的“加权平均”和选择相结合的系数选择策略.最后, 对得到的融合系数进行逆变换得到融合图像.通过实验可以发现相比于传统的图像融合方法, 本文方法得到了更高的客观指标, 融合图像视觉效果更好.

Abstract

To overcome the shortcoming of traditional image fusion method based on multi-scale transform, a novel adaptive image fusion algorithm based on shift-invariant shearlet transform (SIST) is proposed. Firstly, the SIST is utilized to decompose the source images, and the low frequency sub-band coefficients and directional bandpass sub-band coefficients are obtained. Secondly, for the low frequency sub-band coefficients, the singular value decomposition method in the gradient domain is used to estimate the local structure information of image, and a variable weights fusion scheme based on the sigmoid function and the extracted features is presented, while for the directional bandpass sub-band coefficients, a scheme based on the Sum-modified-Laplacian (SML) combined with the weighted average scheme is presented. Finally, the fused image is obtained by performing the inverse SIST on the combined coefficients. The experimental results show that the proposed approach can significantly outperform the conventional image fusion methods in terms of both objective evaluation criteria and visual quality.

参考文献

[1] BLUM R S, LIU Z. Multi-sensor image fusion and its applications［M］. Boca Raton: CRC Press, 2005: 1-10.

[2] HUANG W, JING Z L. Evaluation of focus measures in multi-focus image fusion［J］. Pattern Recognition Letters, 2007, 28(4): 493-500.

[3] LI S T, YANG B. Multi-focus image fusion using region segmentation and spatial frequency［J］. Image and Vision Computing, 2008, 26(7): 971-979.

[4] LI S T, KWOK J T, WANG Y N. Using the discrete wavelet frame transform to merge Landsat TM and SPOT panchromatic images［J］. Information Fusion, 2002, 3(1): 17-23.

[5] WANG H H. A new multiwavelet-based approach to image fusion［J］. Journal of Mathematical Imaging and Vision, 2004, 21(2): 177-192.

[6] LEWIS J J, O′CALLAGHAN R J, NIKOLOV S G, et al. Pixel-and region-based image fusion with complex wavelets［J］. Information Fusion, 2007, 8(2): 119-130.

[7] LI W, ZHU X F. An image fusion algorithm based on second generation wavelet transform and its performance evaluation［J］. Acta Aotomatica Sinica, 2007, 33(8): 817-822.

[8] 焦李成,谭山. 图像的多尺度几何分析: 回顾和展望［J］. 电子学报,2003,31(12A): 1975-1981.

JIAO Li-cheng, TAN Shan. Development and prospect of image multiscale geometric analysis［J］. Acta Electronica Sinica, 2003, 31(12A): 1975-1981.

[9] DO M N, VETTERLI M. The Contourlet transform: an efficient directional multiresolution image representation［J］. IEEE Transactions on Image Processing, 2005, 14(12): 2091-2106.

[10] MIAO Q G, WANG B S. A novel image fusion method using contourlet transform［C］. Proceedings 2006 International Conference on Communications, Circuits and Systems Processing, 2006, 548-552.

[11] YANG L, GUO B L, NI W. Multimodality medical image fusion based on multiscale geometric analysis of Contourlet transform［J］. Neurocomputing, 2008, 72(1/3): 203-211.

[12] CUNHA A L, ZHOU J P, DO M N. The Nonsubsampled Contourlet Transform: theory, design and application［J］. IEEE Transactions on Image Processing, 2006, 15(10): 3089-3101.

[13] ZHANG Q, GUO B L. Multifocus fusion using the nonsubsampled contourlet transform［J］. Signal Processing, 2009, 89(7): 1334-1346.

[14] LI H F, CHAI Y, LI Z F. Multi-focus image fusion based on nonsubsampled contourlet transform and focused regions detection［J］. Optik-International Journal for Light and Electron Optics, 2013, 124(1): 40-51.

[15] GUO K, LABATE D. Optimally sparse multidimensional representation using shearlets［J］. SIAM Journal on Mathematical Analysis, 2007, 39(1): 298-318.

[16] EASLEY G, LABATE D, LIM W Q. Sparse directional image representations using the discrete shearlet transform［J］. Applied and Computational Harmonic Analysis, 2008, 25(1): 25-46.

[17] YI S, LABATE D, EASLEY G R, et al. A shearlet approach to edge analysis and detection ［J］. IEEE Transactions on Image Processing, 2009, 18(5): 929-941.

[18] DENG C, WANG S, CHEN X. Remote sensing images fusion algorithm based on shearlet transform［C］. In: Proceeding of International Conference on Environmental Science and Information Application Technology. ACM, WuHan, China, 2009: 451-454.

[19] MIAO Q G, SHI C, XU P F, et al. A novel algorithm of image fusion using shearlets［J］. Optics Communications, 2011, 284(6): 1540-1547.

[20] WANG L, LI Bin, TIAN L F. Multi-modal medical image fusion using the inter-scale and intra-scale dependencies between image shift-invariant shearlet coefficients［J/OL］. Information Fusion, 2012［2012-10-22］. http: //dx.doi.org/10.1016/j.inffus.2012.03.002.

[21] ZHANG Z, BLUM R S. A categorization of multiscale-decomposition-based image fusion schemes with a performance study for a digital camera application ［J］. Proceedings of the IEEE, 1999, 87(8): 1315-1326.

[22] TAKEDA H, FARSIU S, MILANFAR P. Kernel regression for image processing and reconstruction［J］. IEEE Transactions on Image Processing, 2007, 16(2): 349-366.

[23] THAIPANICH T, OH B T, WU P H, et al. Improved image denoising with adaptive Nonlocal means (ANL-Means) algorithm［J］. IEEE Transactions on Consumer Electronics, 2010, 56(4): 2623-2630.

[24] 许光宇, 檀结庆, 钟金琴. 自适应的有效非局部图像滤波［J］. 中国图象图形学报, 2012, 17(4): 471-479.

XU Guang-yu, TAN Jie-qing, ZHONG Jin-qin. Adaptive efficient non-local image filtering［J］. Journal of Image and Graphics, 2012, 17(4): 471-479.

[25] LI T J, WANG Y Y. Biological image fusion using a NSCT based variable-weight method［J］. Information Fusion, 2011, 12(2): 85-92.

[26] 胡钢, 吉晓民, 刘哲等. 结合区域特性和非子采样SPT的图像融合方法［J］. 计算机辅助设计与图形学学报, 2012,24(5): 637-648.

HU Gang, JI Xiao-min, LIU Zhe, et al. Regional feature self-adaptive image fusion method based on nonsubsampled steerable pyramid transform［J］. Journal of Computer-Aided Design & Computer Graphics, 2012, 24(5): 637-648.

[27] QU G H, ZHANG D L, YAN P F. Information measure for performance of image fusion［J］. Electronics Letters, 2002, 38(7): 313-315.

[28] PETROVIC V, XYDEAS C. On the effects of sensor noise in pixel-level image fusion performance［C］. Proceedings Of the Third International Conference on Information Fusion, IEEE Press, 2000, 2: 14-19.

刘卫, 殷明, 栾静, 郭宇. 基于平移不变剪切波变换域图像融合算法[J]. 光子学报, 2013, 42(4): 496. LIU Wei, YIN Ming, LUAN Jing, GUO Yu. Image Fusion Algorithm Based on Shift-invariant Shearlet Transform[J]. ACTA PHOTONICA SINICA, 2013, 42(4): 496.

基于平移不变剪切波变换域图像融合算法

关于本站 Cookie 的使用提示

全站搜索

基于平移不变剪切波变换域图像融合算法

相关论文

相关资讯

关于本站 Cookie 的使用提示

全站搜索