Efficient image fusion with approximate sparse representation

In this paper, an efficient approximate sparse representation (SR) algorithm with multi-selection strategy is used to solve the image fusion problem. We have shown that the approximate SR is effective for image fusion even if the sparse coefficients are not the sparsest ones possible. A multi-selection strategy is used to accelerate the process of generating the approximate sparse coefficients which are used to guide the fusion of image patches. The relative parameters are also investigated experimentally to further reduce the computational time. The proposed method is compared with some state-of-the-art image fusion approaches on several pairs of multi-source images. The experimental results exhibit that the proposed method is able to yield superior fusion results with less consumption time.

[1]  M. Elad,et al.  $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.

[2]  Tao Chen,et al.  Remote sensing image fusion based on ridgelet transform , 2005, Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05..

[3]  Jinbo Li,et al.  Regional multifocus image fusion using sparse representation. , 2013, Optics express.

[4]  Uday B. Desai,et al.  Fusion of Surveillance Images in Infrared and Visible Band Using Curvelet, Wavelet and Wavelet Packet Transform , 2010, Int. J. Wavelets Multiresolution Inf. Process..

[5]  Shutao Li,et al.  Multifocus Image Fusion and Restoration With Sparse Representation , 2010, IEEE Transactions on Instrumentation and Measurement.

[6]  Ping Guo,et al.  Image Fusion by Hierarchical Joint Sparse Representation , 2013, Cognitive Computation.

[7]  Hanseok Ko,et al.  Multimodal image fusion via sparse representation with local patch dictionaries , 2013, 2013 IEEE International Conference on Image Processing.

[8]  Zhenhong Jia,et al.  A novel multi-focus image fusion method using PCNN in nonsubsampled contourlet transform domain , 2015 .

[9]  Nong Sang,et al.  Attention-based hierarchical fusion of visible and infrared images , 2015 .

[10]  Gaurav Bhatnagar,et al.  An Image Fusion Framework Based on Human Visual System in Framelet Domain , 2012, Int. J. Wavelets Multiresolution Inf. Process..

[11]  Shutao Li,et al.  Color image fusion with extend joint sparse model , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[12]  Edward H. Adelson,et al.  The Laplacian Pyramid as a Compact Image Code , 1983, IEEE Trans. Commun..

[13]  Shutao Li,et al.  Visual attention guided image fusion with sparse representation , 2014 .

[14]  Y. C. Pati,et al.  Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition , 1993, Proceedings of 27th Asilomar Conference on Signals, Systems and Computers.

[15]  Michael Elad,et al.  From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images , 2009, SIAM Rev..

[16]  Shutao Li,et al.  Multimodal image fusion with joint sparsity model , 2011 .

[17]  Shutao Li,et al.  Remote Sensing Image Fusion via Sparse Representations Over Learned Dictionaries , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Shutao Li,et al.  Pixel-level image fusion with simultaneous orthogonal matching pursuit , 2012, Inf. Fusion.

[19]  Zhaowei Shang,et al.  Image Fusion Method Based on Multi-Directional Support Value Transform , 2012, Int. J. Wavelets Multiresolution Inf. Process..

[20]  Shutao Li,et al.  Image Fusion With Guided Filtering , 2013, IEEE Transactions on Image Processing.

[21]  Seiichi Mita,et al.  Image Fusion Based on Multi-objective Optimization , 2014, Int. J. Wavelets Multiresolution Inf. Process..

[22]  Rick S. Blum,et al.  A categorization of multiscale-decomposition-based image fusion schemes with a performance study for a digital camera application , 1999, Proc. IEEE.

[23]  Henk J. A. M. Heijmans,et al.  A new quality metric for image fusion , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[24]  Shutao Li,et al.  Group-Sparse Representation With Dictionary Learning for Medical Image Denoising and Fusion , 2012, IEEE Transactions on Biomedical Engineering.

[25]  Vladimir Petrovic,et al.  Objective image fusion performance measure , 2000 .