Fast Image Fusion Algorithm Based on Sparse Representation and Non-subsampled Contourlet Transform

ZHAO Chunhui; GUO Yunting

doi:10.11999/JEIT150933

Volume 38 Issue 7

Jul. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(7): 1773-1780

ZHAO Chunhui, GUO Yunting. Fast Image Fusion Algorithm Based on Sparse Representation and Non-subsampled Contourlet Transform[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1773-1780. doi: 10.11999/JEIT150933

Citation:

ZHAO Chunhui, GUO Yunting. Fast Image Fusion Algorithm Based on Sparse Representation and Non-subsampled Contourlet Transform[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1773-1780. doi: 10.11999/JEIT150933

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Fast Image Fusion Algorithm Based on Sparse Representation and Non-subsampled Contourlet Transform

doi: 10.11999/JEIT150933 cstr: 32379.14.JEIT150933

ZHAO Chunhui¹,
GUO Yunting¹

Funds:

The National Natural Science Foundation of China (61571145, 61405041), The Key Program of Heilongjiang Province Natural Science Foundation (ZD201216), Excellent Academic Leaders Program of Harbin (RC2013XK009003)

Received Date: 2015-08-13
Rev Recd Date: 2016-04-07
Publish Date: 2016-07-19

Abstract

Abstract

In order to improve the efficiency and quality of image fusion, a new image fusion algorithm based on four-direction Sparse Representation (SR) and fast Non-Subsampled Contourlet Transform (NSCT) is proposed. The proposed method firstly provides a series of low- and high-frequency sub-bands of source images via fast NSCT decomposition. Then adaptive DCT over-complete dictionary is used for the fast four-direction sparse representation and coefficients fusion of low-pass sub-band, while Gaussian weighted regional energy based fusion rule are used in high-pass sub-bands. Fast NSCT modifies the tree structure filter bank of traditional NSCT into multi-channel structure, and it saves about half of the time. The fast SR fusion method adopts a four-direction sparse representation for coefficients fusion instead of traditional sliding window method, and further improves the efficiency of algorithm. The experimental results show that the proposed fast fusion algorithm can improve the efficiency nearly 20 times without sacrificing fusion quality.
- Image processing,
- Image fusion,
- Non-Subsampled Contourlet Transform (NSCT),
- Sparse Representation (SR),
- Fast algorithm

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References(21)

References

BURT P J and ANDELSON E H. The Laplacian pyramid as a compact image code[J]. IEEE Transactions on Communications, 1983, 31(4): 532-540. doi: 10.1109/TCOM. 1983.1095851.

LI H, MANJUNATH B, and ITRA S. Multisensor image fusion using the wavelet transform[J]. Graphical Models and Image Processing, 1995, 57(3): 235-245.

MINH N D and MARTIN V. The finite ridgelet transform for image representation[J]. IEEE Transactions on Image Processing, 2003, 12(1): 16-28. doi: 10.1109/TIP.2002.806252.

NENCINI F, GARZELLI A, BARONTI S, et al. Remote sensing image fusion using the curvelet transform[J]. Information Fusion, 2007, 8(2): 143-156.

CUNHA A L, ZHOU J, and DO M N. The nonsubsampled contourlet transform: theory, design, and applications[J]. IEEE Transactions on Image Processing, 2006, 15(10): 3089-3101. doi: 10.1109/TIP.2006.877507.

DO M N and VETTERLI M. The contourlet transform: an efficient directional multiresolution image representation[J]. IEEE Transactions on Image Processing, 2005, 14(12): 2091-2106. doi: 10.1109/TIP.2005.859376.

杨晓慧, 贾建, 焦李成. 基于活性测度和闭环反馈的非下采样Contourlet域图像融合[J]. 电子与信息学报, 2010, 32(2): 422-426. doi: 10.3724/SP.J.1146.2008.01038.

YANG Xiaohui, JIA Jian, and JIAO Licheng. Image fusion algorithm in nonsubsampled contourlet domain based on activity measure and closed loop feedback[J]. Journal of Electronics Information Technology, 2010, 32(2): 422-426. doi: 10.3724/SP.J.1146.2008.01038.

赵春晖, 马丽娟, 邵国锋. 采用WA-WBA与改进INSCT的图像融合算法[J]. 电子与信息学报, 2014, 36(2): 304-311. doi: 10.3724/SP.J.1146.2013.00542.

ZHAO Chunhui, MA Lijuan, and SHAO Guofeng. An image fusion algorithm based on WA-WBA and improved non- subsampled contourlet transform[J]. Journal of Electronics Information Technology, 2014, 36(2): 304-311. doi: 10.3724/ SP.J.1146.2013.00542.

YANG B and LI S. Pixel-level image fusion with simultaneous orthogonal matching pursuit[J]. Information

Fusion, 2012, 13(1): 10-19.

LIU Y, LIU S, and WANG Z. A general framework for image fusion based on multi-scale transform and sparse representation[J]. Information Fusion, 2015, 24(1): 147-164.

ZHENG Wei, SUN Xueqing, HAO Dongmei, et al. Thyroid image fusion based on shearlet transform and sparse representation[J]. Opto-Electronic Engineering, 2015, 42(1): 77-83.

YAGHOOBI M, WU D, and DAVIES M E. Fast non-negative orthogonal matching pursuit[J]. IEEE Signal Processing Letters, 2015, 22(9): 1229-1233. doi: 10.1109/LSP.2015. 2393637.

ZHENG Hao and TAO Dapeng. Discriminative dictionary learning via Fisher discrimination K-SVD algorithm[J]. Neurocomputing, 2015, 2015(162): 9-15.

ZHAO Chunhui, GUO Yunting, and WANG Yulei. A fast fusion scheme for infrared and visible light images in NSCT domain[J]. Infrared Physics Technology, 2015, 2015(72): 266-275.

首照宇, 胡蓉, 欧阳宁, 等. 基于多尺度稀疏表示的图像融合方法[J]. 计算机工程与设计, 2015, 36(1): 232-235. doi: 10.16208/j.issn1000-7024.2015.01.042.

SHOU Zhaoyu, HU Rong, OUYANG Ning, et al. Image fusion based on multi-scale sparse representation[J]. Computer Engineering and Design, 2015, 36(1): 232-235. doi: 10.16208/j.issn1000-7024.2015.01.042.

YIN H T, LI S T, and FANG L Y. Simultaneous image fusion and super-resolution using sparse representation[J]. Information Fusion, 2013, 14(3): 229-240.

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

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