Matrix-R Theory: A Simple Generic Method to Improve RGB-Guided Spectral Recovery Algorithms †
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| Publicado en: | Sensors vol. 25, no. 24 (2025), p. 7662-7687 |
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| Autor principal: | |
| Otros Autores: | , |
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MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 1424-8220 | ||
| 024 | 7 | |a 10.3390/s25247662 |2 doi | |
| 035 | |a 3286351912 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231630 |2 nlm | ||
| 100 | 1 | |a Finlayson, Graham D | |
| 245 | 1 | |a Matrix-R Theory: A Simple Generic Method to Improve RGB-Guided Spectral Recovery Algorithms † | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a RGB-guided spectral recovery algorithms include both spectral reconstruction (SR) methods that map image RGBs to spectra and pan-sharpening (PS) methods, where an RGB image is used to guide the upsampling of a low-resolution spectral image. In this paper, we exploit Matrix-R theory in developing a post-processing algorithm that, when applied to the outputs of any and all spectral recovery algorithms, almost always improves their spectral recovery accuracy (and never makes it worse). In Matrix-R theory, any spectrum can be decomposed into a component—called the fundamental metamer—in the space spanned by the spectral sensitivities and a second component—the metameric black—that is orthogonal to this subspace. In our post-processing algorithm, we substitute the correct fundamental metamer, which we calculate directly from the RGB image, for the estimated (and generally incorrect) fundamental metamer that is returned by a spectral recovery algorithm. Significantly, we prove that substituting the correct fundamental metamer always reduces the recovery error. Further, if the spectra in a target application are known to be well described by a linear model of low dimension, then our Matrix-R post-processing algorithm can also exploit this additional physical constraint. In experiments, we demonstrate that our Matrix-R post-processing improves the performance of a variety of spectral reconstruction and pan-sharpening algorithms. | |
| 653 | |a Decomposition | ||
| 653 | |a Cameras | ||
| 653 | |a Methods | ||
| 653 | |a Deep learning | ||
| 653 | |a Algorithms | ||
| 700 | 1 | |a Yi-Tun, Lin | |
| 700 | 1 | |a Kucuk Abdullah | |
| 773 | 0 | |t Sensors |g vol. 25, no. 24 (2025), p. 7662-7687 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3286351912/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3286351912/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3286351912/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |