Fast Fusion of Multi-Band Images Based on Solving a Sylvester Equation
By Qi Wei, Nicolas Dobigeon, Jean-Yves Tourneret. IEEE Transactions on Image Processing, 2015
468 citations recorded by the source corpus.
This paper proposes a fast multi-band image fusion algorithm, which combines a high-spatial low-spectral resolution image and a low-spatial high-spectral resolution image. The well admitted forward model is explored to form the likelihoods of the observations. Maximizing the likelihoods leads to solving a Sylvester equation. By exploiting the properties of the circulant and downsampling matrices associated with the fusion problem, a closed-form solution for the corresponding Sylvester equation is obtained explicitly, getting rid of any iterative update step. Coupled with the alternating direction method of multipliers and the block coordinate descent method, the proposed algorithm can be easily generalized to incorporate prior information for the fusion problem, allowing a Bayesian estimator. Simulation results show that the proposed algorithm achieves the same performance as the existing algorithms with the advantage of significantly decreasing the computational complexity of these algorithms.
Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Media Technology
View Paper: https://www.semanticscholar.org/paper/73ccf074ffb10ef0516ced16fbe9a2f271e8b7b9
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Mechanics
- Shutao Li
- Leyuan Fang
- Renwei Dian
- Hassan Ghassemian
- Jocelyn Chanussot
- Naoto Yokoya
- Claas Grohnfeldt
- José M. Bioucas-Dias
In Computer Vision and Pattern Recognition
- Leyuan Fang
- Shutao Li
- Renwei Dian
- Hassan Ghassemian
- Jocelyn Chanussot
- Naoto Yokoya
- Nicolas Dobigeon
- Qi Wei
In Media Technology
- Shutao Li
- Leyuan Fang
- Renwei Dian
- Jocelyn Chanussot
- Hassan Ghassemian
- Naoto Yokoya
- José M. Bioucas-Dias
- Nicolas Dobigeon
Citations in the corpus
- A review of remote sensing image fusion methods (2016, methodology): “A fast multi-band image fusion is proposed in [84], which forms the likelihoods of the observations.”
- Fusing Hyperspectral and Multispectral Images via Coupled Sparse Tensor Factorization (2018, methodology): “A IKONOS-like spectral reflectance response filter [18] is used to generate the four-band HR-MSI with the size of 256 × 256 × 4.”
- Hyperspectral Pansharpening: A Review (2015, methodology): “Finally, very recently, a Sylvester equation-based explicit solution of the related optimization problem has been derived in [61], [87] leading to a significant decrease of the computational complexity.”
- Learning a Low Tensor-Train Rank Representation for Hyperspectral Image Super-Resolution (2019, methodology): “Therefore, we force the derivative of (25) with regard to Z ( 3 ) to be zero, and obtain the following equation : where where 26) analytically and efficiently as demonstrated in [45].”
- Hyperspectral and Multispectral Data Fusion: A comparative review of the recent literature (2017, methodology): “This observation can be explained by the fact that CNMF, ECCV’14, ICCV’15, HySure, FUSE, and FUSE-S all operate on a subspace.”
- Deep Hyperspectral Image Sharpening (2018, unknown): “This assumption has been widely used in the HSI sharpening literatures [1], [26], [40], [45].”