R-FUSE: Robust Fast Fusion of Multi-Band Images Based on Solving a Sylvester Equation
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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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Apr 2016.
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Citing papers per year: 2021: 3; 2022: 0; 2023: 1.
4 papers.
- 2021Fast and High-Quality Blind Multi-Spectral Image Pansharpeningcited by 13
- 2021Coupling Model-Driven and Data-Driven Methods for Remote Sensing Image Restoration and Fusioncited by 11
- 2021A Triple-Double Convolutional Neural Network for Panchromatic Sharpeningcited by 6
- 2023SDRCNN: A single-scale dense residual connected convolutional neural network for pansharpeningcited by 1
- 2023SDRCNN: A single-scale dense residual connected convolutional neural network for pansharpening1 Jul 2023
- 2021A Triple-Double Convolutional Neural Network for Panchromatic Sharpening4 Dec 2021
- 2021Coupling Model-Driven and Data-Driven Methods for Remote Sensing Image Restoration and Fusion13 Aug 2021
- 2021Fast and High-Quality Blind Multi-Spectral Image Pansharpening17 Mar 2021