# R-FUSE: Robust Fast Fusion of Multi-Band Images Based on Solving a Sylvester Equation

arXiv: https://arxiv.org/abs/1604.01818 · HTML: https://mlsanity.com/paper/1604.01818/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2016.

## Built on by (most cited first, 4 of 4)

- [Fast and High-Quality Blind Multi-Spectral Image Pansharpening](https://mlsanity.com/paper/2103.09943/) (2021)
- [Coupling Model-Driven and Data-Driven Methods for Remote Sensing Image Restoration and Fusion](https://mlsanity.com/paper/2108.06073/) (2021)
- [A Triple-Double Convolutional Neural Network for Panchromatic Sharpening](https://mlsanity.com/paper/2112.02237/) (2021)
- [SDRCNN: A single-scale dense residual connected convolutional neural network for pansharpening](https://mlsanity.com/paper/2307.00327/) (2023)

## Abstract (arXiv)

This paper proposes a robust fast multi-band 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 in [1], the generalized Sylvester matrix equation associated with the multi-band 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 [1]. 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 in [1]. The proposed new algorithm is tested with different priors considered in [1]. Our conclusion is that the proposed fusion algorithm is more robust than the one in [1] with a reduced computational cost.