一種快速的基于稀疏表示和非下采樣輪廓波變換的圖像融合算法

趙春暉; 郭蘊(yùn)霆

doi:10.11999/JEIT150933

一種快速的基于稀疏表示和非下采樣輪廓波變換的圖像融合算法

doi: 10.11999/JEIT150933

趙春暉¹,
郭蘊(yùn)霆¹

基金項(xiàng)目:

國(guó)家自然科學(xué)基金(61571145, 61405041)，黑龍江省自然科學(xué)基金重點(diǎn)資助項(xiàng)目(ZD201216)，哈爾濱市優(yōu)秀學(xué)科帶頭人資金(RC2013XK009003)

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出版歷程
- 收稿日期: 2015-08-13
- 修回日期: 2016-04-07
- 刊出日期: 2016-07-19

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

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)

摘要

摘要: 為了提高圖像融合的效率和質(zhì)量，該文提出一種基于快速非下采樣輪廓波變換(NSCT)和4方向稀疏表示的圖像融合算法。該方法首先對(duì)源圖像進(jìn)行快速NSCT分解，生成一系列低通和高通子帶。對(duì)于低頻子帶，利用自適應(yīng)生成的DCT過(guò)完備字典進(jìn)行快速的4方向稀疏表示和系數(shù)融合；對(duì)于高頻子帶，則利用高斯加權(quán)區(qū)域能量最大的融合規(guī)則進(jìn)行系數(shù)融合?？焖貼SCT將傳統(tǒng)NSCT的樹(shù)形濾波結(jié)構(gòu)轉(zhuǎn)變?yōu)槎嗤ǖ罏V波結(jié)構(gòu)，能成倍提高分解效率；快速的稀疏融合則拋棄了傳統(tǒng)的滑動(dòng)窗口方法，以水平、垂直、對(duì)角線(xiàn)4個(gè)方向進(jìn)行稀疏表示和稀疏融合，進(jìn)一步提高算法效率。實(shí)驗(yàn)結(jié)果表明，提出的快速算法能在不影響融合質(zhì)量的條件下將算法效率提高近20倍。
- 圖像處理 /
- 圖像融合 /
- 非下采樣輪廓波變換 /
- 稀疏表示 /
- 快速算法
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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參考文獻(xiàn)(21)

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.

楊曉慧, 賈建, 焦李成. 基于活性測(cè)度和閉環(huán)反饋的非下采樣Contourlet域圖像融合[J]. 電子與信息學(xué)報(bào), 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.

趙春暉, 馬麗娟, 邵國(guó)鋒. 采用WA-WBA與改進(jìn)INSCT的圖像融合算法[J]. 電子與信息學(xué)報(bào), 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, 歐陽(yáng)寧, 等. 基于多尺度稀疏表示的圖像融合方法[J]. 計(jì)算機(jī)工程與設(shè)計(jì), 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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