In order to reduce the influence of mixed pixel on dictionary, the author has constituted principal component analysis (PCA)united sparse dictionary from the first principal component extracted with sparse dictionary of panchromatic image and unmiximg image by the online dictionary learning algorithm and PCA. The sparse dictionary can include multi-spectral image and high-spatial resolution image features, while considering the mixed pixel problem. The sparse coefficients of panchromatic and multi-spectral images are calculated using PCA united sparse dictionary and orthogonal matching pursuit(OMP) algorithm, then the sparse coefficients of fusion image are calculated using nonnegative matrix factor(NMF) fusion algorithm, thus reconstructing fusion image. In consideration of the root mean square error of the reconstructed image and the limitation of computing, research on the dictionary matrix size shows that the final matrix size of sparse dictionary is 64×480. An analysis of five quantitative assessment indexes demonstrates that more texture details and multi-spectral information can be obtained by the proposed fusion than by united sparse dictionary NMF fusion, wavelet fusion and PCA fusion. The proposed method can obtain better fusion result.
Liang R H, Cheng L Z . Double sparse image representation via learning dictionaries in wavelet domain[J]. Journal of National University of Defense Technology, 2012,34(4):126-131.
[3]
Iqbal M, Chen J . Unification of image fusion and super-resolution using jointly trained dictionaries and local information contents[J]. IET Image Processing, 2012,6(9):1299-1310.
doi: 10.1049/iet-ipr.2012.0122
Sun Y B, Wei Z H, Xiao L , et al. Multimorphology sparsity regularized image super-resolution[J]. Acta Electronica Sinica, 2010,38(12):2898-2903.
[5]
Liu Y, Wang Z F. Multi-focus image fusion based on sparse representation with adaptive sparse domain selection[C]// 2013 Seventh International Conference on Image and Graphics.Qingdao:IEEE, 2013: 591-596.
[6]
Huang B, Song H H, Cui H B , et al. Spatial and spectral image fusion using sparse matrix factorization[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013,52(3):1693-1704.
doi: 10.1109/TGRS.2013.2253612
[7]
Song H H, Huang B, Liu Q S , et al. Improving the spatial resolution of Landsat TM/ETM+ through fusion with SPOT5 images via learning-based super-resolution[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014,53(3):1195-1204.
doi: 10.1109/TGRS.2014.2335818
[8]
Li S T, Yin H T, Fang L Y . Remote sensing image fusion via sparse representations over learned dictionaries[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013,51(9):4779-4789.
doi: 10.1109/TGRS.2012.2230332
Zhang Y, Xu B, Zhou S B , et al. Image super-resolution with adaptive regularization sparse representation[J]. Application Research of Computers, 2013,30(3):938-941.
Xue M G, Xu G M . Hallucinating faces reconstruction method via centralized sparse representation based on clustered dictionary[J]. Systems Engineering and Electronics, 2014,36(1):187-193.
He T D, Li J W . Hyperspectral remote sensing image classification based on adaptive sparse representation[J]. Systems Engineering and Electronics, 2013,35(9):1994-1998.
Wang F, Liang X G, Cui Y K , et al. Image fusion combined with NMF and new contourlet transform[J]. Computer Engineering and Applications, 2013,49(5):150-153.