Blind model-based fusion of multi-band and panchromatic images

This paper proposes a blind model-based fusion method to combine a low-spatial resolution multi-band image and a high-spatial resolution panchromatic image. This method is blind in the sense that the spatial and spectral responses in the degradation model are unknown and estimated from the observed data pair. The Gaussian and total variation priors have been used to regularize the ill-posed fusion problem. The formulated optimization problem associated with the image fusion can be attacked efficiently using a recently developed robust multi-band image fusion algorithm in [1]. Experimental results including qualitative and quantitative ones show that the fused image can combine the spectral information from the multi-band image and the high spatial resolution information from the panchromatic image effectively with very competitive computational time.

[1]  Simon J. Godsill,et al.  R-FUSE: Robust Fast Fusion of Multiband Images Based on Solving a Sylvester Equation , 2016, IEEE Signal Processing Letters.

[2]  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.

[3]  Lionel Moisan,et al.  No-Reference Image Quality Assessment and Blind Deblurring with Sharpness Metrics Exploiting Fourier Phase Information , 2015, Journal of Mathematical Imaging and Vision.

[4]  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).

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

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

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

[8]  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.

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

[10]  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.

[11]  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).

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

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

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