香农熵加权稀疏表示图像融合方法研究

李奕; 吴小俊

doi:10.3724/SP.J.1004.2014.01819

香农熵加权稀疏表示图像融合方法研究

doi: 10.3724/SP.J.1004.2014.01819

李奕^1,2,
吴小俊^1, ,

1.
江南大学物联网工程学院无锡 214122;
2.
青岛大学国际教育合作学院青岛 266071

基金项目:

国家自然科学基金（60973094，61103128，61373055），教育部科学技术研究重大项目（311024）

详细信息

作者简介:
李奕江南大学物联网工程学院博士研究生. 主要研究方向为模式识别，智能计算及应用.E-mail：lyqgx@126.com

通讯作者:
吴小俊江南大学物联网工程学院教授．主要研究方向为人工智能，模式识别和计算机视觉.E-mail：wu xiaojun@aliyun.com

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- 文章访问数: 2050
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- 被引次数: 0
出版历程
- 收稿日期: 2013-04-10
- 修回日期: 2014-02-17
- 刊出日期: 2014-08-20

Image Fusion Based on Sparse Representation Using Shannon Entropy Weighting

LI Yi^1,2,
WU Xiao-Jun^{1
, ,}

1.
School of Internet of Things Engineering, Jiangnan University, Wuxi 214122;
2.
College of International Cooperation in Education, Qingdao University, Qingdao 266071

Funds:

Supported by National Natural Science Foundation of China (60973094, 61103128, 61373055), and Key Grant Project of Chinese Ministry of Education (311024)

摘要

摘要: 针对传统稀疏表示同步超分图像融合模型中对于 LL （Low-low frequency）、LH （Low-high frequency）、H （High frequency）三部分等比例加权，不能突出重点信息之不足，本文提出一种香农熵多视角加权稀疏表示同步超分图像融合方法. 该方法引入香农熵加权技术，针对 LL、LH、H三部分根据图像特征进行自适应加权，突出重点频率段的影响，从而提高了图像融合的效果. 在多组不同类型图像上进行了实验，实验结果表明所提方法无论从融合视觉效果还是评价指标上均显示出有效性.
- 香农熵 /
- 多视角加权 /
- 稀疏表示 /
- 图像融合
Abstract: In order to overcome the shortcoming caused by the equal weighting on LL (Low-low frequency) components, LH (Low-high frequency) components of low resolution images and H (High frequency) components in the sparse representation image fusion model, a novel image fusion method of Shannon entropy multi-view weighting based on sparse representation is proposed. The proposed method can assign different weights to LL, LH and H components, and adaptively enhance the influences of the important components. Thus, the image fusion effect can be improved effectively. Experimental results obtained on different kinds of images indicate that the proposed method can achieve the promising performance in terms of both visual quality and quantitative evaluation metrics.
- Shannon entropy /
- multi-view weighting /
- sparse representation /
- image fusion

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参考文献(43)

[1]	Chipman L J, Orr T M, Graham L N. Wavelets and image fusion. In: Proceedings of the 4th International Conference on Image Processing. Washington D.C., USA: IEEE, 1995. 248-251
[2]	[2] Li H, Manjunath B S, Mitra S K. Multi-sensor image fusion using the wavelet transform. In: Proceedings of the 1994 IEEE International Conference Image Processing. Austin, USA: IEEE, 1994, 1: 51-55
[3]	[3] Pajares G, Manuel J. A wavelet-based image fusion tutorial. Parttern Recognition, 2004, 37(9): 1855-1872
[4]	[4] Li Z H, Jing Z L, Yang X H, Sun S Y. Color transfer based remote sensing image fusion using non-separable wavelet frame transform. Parttern Recognition Letters, 2005, 26(13): 2006-2014
[5]	[5] Li S T, Yang B. Multifocus image fusion by combining curvelet and wavelet transform. Parttern Recognition Letters, 2008, 29(9): 1295-1301
[6]	[6] Zhang Q, Guo B L. Multifocus image fusion using the nonsubsampled contourlet transform. Signal Processing, 2009, 89(7): 1334-1346
[7]	[7] Yang S Y, Wang M, Jiao L C, Wu R X, Wang Z X. Image fusion based on a new contourlet packet. Information Fusion, 2010, 11(2): 78-84
[8]	[8] Li S T, Yang B, Hu J W. Performance comparison of different multi-resolution transforms for image fusion. Information Fusion, 2011, 12(2): 74-84
[9]	[9] Mukane S M, Ghodake Y S, Khandagle P S. Image enhancement using fusion by wavelet transform and Laplacian pyramid. Computer Science, 2013, 10(4): 122-126
[10]	Jiang Y, Wang M H. Image fusion with morphological component analysis. Information Fusion, 2014, 18: 107-118
[11]	Balakrishnan S, Cacciola M, Udpa L, Rao B P, Jayakumar T, Raj B. Development of image fusion methodology using discrete wavelet transform for eddy current images. NDT and E International, 2012, 51: 51-57
[12]	Petrovic V S, Xydeas C S. Gradient-based multiresolution image fusion. IEEE Transactions on Image Processing, 2004, 13(2): 228-237
[13]	Borwonwatanadelok P, Rattanapitak W, Udomhunsakul S. Multi-focus image fusion based on stationary wavelet transform and extended spatial frequency measurement. In: Proceedings of the 2009 International Conference on Electronic Computer Technology. Macau, China: IEEE, 2009. 77-81
[14]	Lewis J J, O'callaghan R J, Nikolov S G, Bull D R, Canagarajah N. Pixel-and-region-based image fusion with complex wavelets. Information Fusion, 2007, 8(2): 119-130
[15]	Miao Q G, Lou J J, Xu P F. Image fusion based on NSCT and bandelet transform. In: Proceedings of the 2012 Computational Intelligence and Security. Guangzhou, China: IEEE, 2012. 314-317
[16]	Li H F, Chai Y, Li Z F. Multi-focus image fusion based on nonsubsampled contourlet transform and focused regions detection. Optik-International Journal for Light and Electron Optics, 2013, 124(1): 40-51
[17]	Candes E S. Compressive Sampling. In: Proceedings of the 2006 International Congress on Mathematicians. Madrid, Spain: European Mathematical Society Publishing House, 2006, 3: 1433-1452
[18]	Liu Fang, Wu Jiao, Yang Shu-Yuan, Jiao Li-Cheng. Research advances on structured compressive sensing. Acta Automatica Sinica, 2013, 39(12): 1980-1995(刘芳, 武娇, 杨淑媛, 焦李成. 结构化压缩感知研究进展. 自动化学报, 2013, 39(12): 1980-1995)
[19]	Ma Xiao-Hu, Tan Yan-Qi. Face recognition based on discriminant sparsity preserving embedding. Acta Automatica Sinica, 2014, 40(1): 73-82 (马小虎, 谭延琪. 基于鉴别稀疏保持嵌入的人脸识别算法. 自动化学报, 2014, 40(1): 73-82)
[20]	Zuo Y Y, Zhang B. Robust hierarchical framework for image classification via sparse representation. Tsinghua Science and Technology, 2011, 16(1): 13-21
[21]	Zhao M, Li S T, Kwok J. Text detection in images using sparse representation with discriminative dictionaries. Image and Vision Computing, 2010, 28(12): 1590-1599
[22]	Yin H T, Li S T, Fang L Y. Simultaneous image fusion and super-resolution using sparse representation. Information Fusion, 2013, 14(3): 229-240
[23]	Yang B, Li S T. Pixel-level image fusion with simultaneous orthogonal matching pursuit. Information Fusion, 2012, 13(1): 10-19
[24]	Zhao Y Q, Yang J X, Zhang Q Y, Song L, Cheng Y M, Pan Q. Hyperspectral imagery super-resolution by sparse representation and spectral regularization. Journal on Advances in Signal Processing, 2011, (1): 87
[25]	Zhao Y, Yang J, Chan J C W. Hyperspectral imagery super-resolution by spatial-spectral joint nonlocal similarity. Selected Topics in Applied Earth Observations and Remote Sensing, 2013, PP(99): 1939-1404
[26]	Lian Qiu-Sheng, Zhang Jun-Qin, Chen Shu-Zhen. Single image super-resolution algorithm based on two-stage and multi-frequency-band dictionaries. Acta Automatica Sinica, 2013, 39(8): 1310-1320 (练秋生, 张钧芹, 陈书贞. 基于两级字典与分频带字典的图像超分辨率算法. 自动化学报, 2013, 39(8): 1310-1320)
[27]	Moussallam M, Daudet L, Richard G. Matching pursuits with random sequential subdictionaries. Signal Processing, 2012, 92(10): 2532-2544
[28]	Hsieh S H, Lu C S, Pei S C. Fast OMP: reformulating OMP via iteratively refining l2-norm solutions. In: Proceedings of the 2012 IEEE Statistical Signal Processing Workshop. Ann Arbor, MI: IEEE, 2012. 189-192
[29]	Peng Yi-Gang, Suo Jin-Li, Dai Qiong-Hai, Xu Wen-Li. From compressed sensing to low-rank matrix recovery: theory and applications. Acta Automatica Sinica, 2013, 39(7): 981-994 (彭义刚, 索津莉, 戴琼海, 徐文立. 从压缩传感到低秩矩阵恢复: 理论与应用. 自动化学报, 2013, 39(7): 981-994)
[30]	Chen L X, Dobra A. Histograms as statistical estimators for aggregate queries. Information Systems, 2013, 38(2): 213-230
[31]	Deng Z H, Choi K S, Chung F L, Wang S T. EEW-SC: Enhanced entropy-weighting subspace clustering for high dimensional gene expression data clustering analysis. Applied. Soft Computing, 2011, 11(8): 4798-4806
[32]	Aharon M, Elad M, Bruckstein A. K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing, 2006, 54(11): 4311-4322
[33]	Bryt O, Elad M. Compression of facial images using the K-SVD algorithm. Visual Communication and Image Representation, 2008, 19(4): 270-282
[34]	Mairal J, Bach F, Ponce J, Sapiro G, Zisserman A. Discriminative learned dictionaries for local image analysis. In: Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition. Anchorage, USA: IEEE, 2008. 1-8
[35]	Engan K, Skretting K, Husoy J H. Family of iterative LS-based dictionary learning algorithms, ILS-DLA, for sparse signal representation. Digital Signal Processing, 2007, 17(1): 32-49
[36]	Divekar A, Ersoy O. Image fusion by compressive sensing. In: Proceedings of the 2009 International Congress on Geoinformatics. Fairfax, VA: IEEE, 2009. 1-6
[37]	Wang Z, Bovik A C. A universal image quality index. IEEE Signal Processing Letters, 2002, 9(3): 81-84
[38]	Piella G, Heijmans H. A new quality metric for image fusion. In: Proceedings of the 2003 IEEE International Conference on Image Processing. Barcelona, Spain: IEEE, 2003, 2: 173-176
[39]	Xydeas C S, Petrovic V. Objective image fusion performance measure. Electronics Letters, 2000, 36(4): 308-309
[40]	Petrovic V. Subjective tests for image fusion evaluation and objective metric validation. Information Fusion, 2007, 8(2): 208-216
[41]	Yang J C, Wright J, Huang T S, Ma Y. Image super-resolution via sparse representation. IEEE Transactions on Image Processing, 2010, 19(11): 2861-2873
[42]	Deng A, Wu J, Yang S. An image fusion algorithm based on discrete wavelet transform and canny operator. In: Proceedings of the 2011 International Congress on Computer Education, Simulation and Modeling. Berlin, Heidelberg: Springer, 2011. 32-38
[43]	Yang B, Li S T. Multifocus image fusion and restoration with sparse representation. IEEE Transactions on Instrumentation and Measurement, 2010, 59(4): 884-892