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
[2]
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
[3]
Zhang Q, Guo B L. Multifocus image fusion using the nonsubsampled contourlet transform. Signal Processing, 2009, 89(7): 1334-1346
[4]
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
[5]
Jiang Y, Wang M H. Image fusion with morphological component analysis. Information Fusion, 2014, 18: 107-118
[6]
Petrovic V S, Xydeas C S. Gradient-based multiresolution image fusion. IEEE Transactions on Image Processing, 2004, 13(2): 228-237
[7]
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
[8]
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
[9]
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)
[10]
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
[11]
Yang B, Li S T. Pixel-level image fusion with simultaneous orthogonal matching pursuit. Information Fusion, 2012, 13(1): 10-19
[12]
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
[13]
Moussallam M, Daudet L, Richard G. Matching pursuits with random sequential subdictionaries. Signal Processing, 2012, 92(10): 2532-2544
[14]
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)
[15]
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
[16]
Bryt O, Elad M. Compression of facial images using the K-SVD algorithm. Visual Communication and Image Representation, 2008, 19(4): 270-282
[17]
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
[18]
Divekar A, Ersoy O. Image fusion by compressive sensing. In: Proceedings of the 2009 International Congress on Geoinformatics. Fairfax, VA: IEEE, 2009. 1-6
[19]
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
[20]
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
[21]
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
[22]
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
[23]
Pajares G, Manuel J. A wavelet-based image fusion tutorial. Parttern Recognition, 2004, 37(9): 1855-1872
[24]
Li S T, Yang B. Multifocus image fusion by combining curvelet and wavelet transform. Parttern Recognition Letters, 2008, 29(9): 1295-1301
[25]
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
[26]
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
[27]
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
[28]
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
[29]
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
[30]
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
[31]
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)
[32]
Zuo Y Y, Zhang B. Robust hierarchical framework for image classification via sparse representation. Tsinghua Science and Technology, 2011, 16(1): 13-21
[33]
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
[34]
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
[35]
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)
[36]
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
[37]
Chen L X, Dobra A. Histograms as statistical estimators for aggregate queries. Information Systems, 2013, 38(2): 213-230
[38]
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
[39]
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
[40]
Wang Z, Bovik A C. A universal image quality index. IEEE Signal Processing Letters, 2002, 9(3): 81-84
[41]
Xydeas C S, Petrovic V. Objective image fusion performance measure. Electronics Letters, 2000, 36(4): 308-309
[42]
Petrovic V. Subjective tests for image fusion evaluation and objective metric validation. Information Fusion, 2007, 8(2): 208-216
[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
