R-FUSE: Robust Fast Fusion of Multiband Images Based on Solving a Sylvester Equation

This letter proposes a robust fast multiband image fusion method to merge a high-spatial low-spectral resolution image and a low-spatial high-spectral resolution image. Following the method recently developed by Wei et al., the generalized Sylvester matrix equation associated with the multiband image fusion problem is solved in a more robust and efficient way by exploiting the Woodbury formula, avoiding any permutation operation in the frequency domain as well as the blurring kernel invertibility assumption required in their method. Thanks to this improvement, the proposed algorithm requires fewer computational operations and is also more robust with respect to the blurring kernel compared with the one developed by Wei et al. The proposed new algorithm is tested with different priors considered by Wei et al. Our conclusion is that the proposed fusion algorithm is more robust than the one by Wei et al. with a reduced computational cost.

[1]  Jean-Yves Tourneret,et al.  Hyperspectral and Multispectral Image Fusion Based on a Sparse Representation , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Jean-Yves Tourneret,et al.  Single image super-resolution of medical ultrasound images using a fast algorithm , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[3]  Chein-I Chang,et al.  Hyperspectral Data Exploitation , 2007 .

[4]  Belur V. Dasarathy,et al.  Medical Image Fusion: A survey of the state of the art , 2013, Inf. Fusion.

[5]  A. Rukhin Matrix Variate Distributions , 1999, The Multivariate Normal Distribution.

[6]  Junfeng Yang,et al.  A New Alternating Minimization Algorithm for Total Variation Image Reconstruction , 2008, SIAM J. Imaging Sci..

[7]  Jean-Yves Tourneret,et al.  Bayesian fusion of multispectral and hyperspectral images using a block coordinate descent method , 2015, 2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).

[8]  Mark L. G. Althouse,et al.  Least squares subspace projection approach to mixed pixel classification for hyperspectral images , 1998, IEEE Trans. Geosci. Remote. Sens..

[9]  A. Basarab,et al.  Fast Single Image Super-resolution using a New Analytical Solution for l2-l2 Problems. , 2016, IEEE transactions on image processing : a publication of the IEEE Signal Processing Society.

[10]  Johannes R. Sveinsson,et al.  Model-Based Satellite Image Fusion , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Richard H. Bartels,et al.  Algorithm 432 [C2]: Solution of the matrix equation AX + XB = C [F4] , 1972, Commun. ACM.

[12]  Chein-I. Chang Hyperspectral Data Exploitation: Theory and Applications , 2007 .

[13]  Jean-Yves Tourneret,et al.  Fast Fusion of Multi-Band Images Based on Solving a Sylvester Equation , 2015, IEEE Transactions on Image Processing.

[14]  Tania Stathaki,et al.  Image Fusion: Algorithms and Applications , 2008 .

[15]  José M. Bioucas-Dias,et al.  Hyperspectral Subspace Identification , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[16]  Jean-Yves Tourneret,et al.  Bayesian Fusion of Multi-Band Images , 2013, IEEE Journal of Selected Topics in Signal Processing.

[17]  Richard G. Baraniuk,et al.  ForWaRD: Fourier-wavelet regularized deconvolution for ill-conditioned systems , 2004, IEEE Transactions on Signal Processing.

[18]  Jean-Yves Tourneret,et al.  Fast Single Image Super-Resolution Using a New Analytical Solution for $\ell _{2}$ – $\ell _{2}$ Problems , 2016, IEEE Transactions on Image Processing.

[19]  Maoguo Gong,et al.  Change Detection in Synthetic Aperture Radar Images based on Image Fusion and Fuzzy Clustering , 2012, IEEE Transactions on Image Processing.

[20]  Naoto Yokoya,et al.  Hyperspectral Pansharpening: A Review , 2015, IEEE Geoscience and Remote Sensing Magazine.

[21]  Jocelyn Chanussot,et al.  A Convex Formulation for Hyperspectral Image Superresolution via Subspace-Based Regularization , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[22]  José M. Bioucas-Dias,et al.  An Augmented Lagrangian Approach to the Constrained Optimization Formulation of Imaging Inverse Problems , 2009, IEEE Transactions on Image Processing.

[23]  David J. Fleming Effect of relative spectral response on multi-spectral measurements and NDVI from different remote sensing systems , 2006 .

[24]  Tongxing Lu,et al.  Solution of the matrix equation AX−XB=C , 2005, Computing.