ResSR: A Computationally Efficient Residual Approach to Super-Resolving Multispectral Images
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866915843727687680 |
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| author | Duba-Sullivan, Haley Reid, Emma J. Voisin, Sophie Bouman, Charles A. Buzzard, Gregery T. |
| author_facet | Duba-Sullivan, Haley Reid, Emma J. Voisin, Sophie Bouman, Charles A. Buzzard, Gregery T. |
| contents | Multispectral imaging (MSI) plays a critical role in material classification, environmental monitoring, and remote sensing. However, MSI sensors typically have wavelength-dependent resolution, which limits downstream analysis. MSI super-resolution (MSI-SR) methods address this limitation by reconstructing all bands at a common high spatial resolution. Existing methods can achieve high reconstruction quality but often rely on spatially-coupled optimization or large learning-based models, leading to significant computational cost and limiting their use in large-scale or time-critical settings.
In this paper, we introduce ResSR, a computationally efficient, model-based MSI-SR method that achieves high-quality reconstruction without supervised training or spatially-coupled optimization. Notably, ResSR decouples spectral and spatial processing into two sequential steps. ResSR first computes a spectrally-informed high-resolution estimate of the MSI using singular value decomposition together with a spatially-decoupled approximate forward model. It then applies a residual correction step to restore low-frequency spatial consistency while preserving high-frequency detail recovered by the spectral reconstruction. ResSR achieves comparable or improved reconstruction quality relative to existing MSI-SR methods while being 2$\times$ to 10$\times$ faster. Code is available at https://github.com/hdsullivan/ResSR. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_13225 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ResSR: A Computationally Efficient Residual Approach to Super-Resolving Multispectral Images Duba-Sullivan, Haley Reid, Emma J. Voisin, Sophie Bouman, Charles A. Buzzard, Gregery T. Image and Video Processing Multispectral imaging (MSI) plays a critical role in material classification, environmental monitoring, and remote sensing. However, MSI sensors typically have wavelength-dependent resolution, which limits downstream analysis. MSI super-resolution (MSI-SR) methods address this limitation by reconstructing all bands at a common high spatial resolution. Existing methods can achieve high reconstruction quality but often rely on spatially-coupled optimization or large learning-based models, leading to significant computational cost and limiting their use in large-scale or time-critical settings. In this paper, we introduce ResSR, a computationally efficient, model-based MSI-SR method that achieves high-quality reconstruction without supervised training or spatially-coupled optimization. Notably, ResSR decouples spectral and spatial processing into two sequential steps. ResSR first computes a spectrally-informed high-resolution estimate of the MSI using singular value decomposition together with a spatially-decoupled approximate forward model. It then applies a residual correction step to restore low-frequency spatial consistency while preserving high-frequency detail recovered by the spectral reconstruction. ResSR achieves comparable or improved reconstruction quality relative to existing MSI-SR methods while being 2$\times$ to 10$\times$ faster. Code is available at https://github.com/hdsullivan/ResSR. |
| title | ResSR: A Computationally Efficient Residual Approach to Super-Resolving Multispectral Images |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2408.13225 |