Matches in SemOpenAlex for { <https://semopenalex.org/work/W4285292786> ?p ?o ?g. }
- W4285292786 endingPage "14" @default.
- W4285292786 startingPage "1" @default.
- W4285292786 abstract "The camera response function (CRF) that projects hyperspectral radiance to the corresponding RGB images is important for most hyperspectral image super-resolution (HSI-SR) models. In contrast to most studies that focus on improving HSI-SR performance through new architectures, we aim to prevent the model performance drop by learning the CRF of any given HSIs and RGB image from the same scene in an unsupervised manner, independent of the HSI-SR network. Accordingly, we first decompose the given RGB image into endmembers and an abundance map using the Dirichlet autoencoder architecture. Thereafter, a linear CRF learning network is optimized to project the reference HSIs to the RGB image that can be similarly decomposed like the given RGB , assuming that objects in both images share the same endmembers and abundance map. The quality of the RGB images generated from the learned CRFs is compared with that of the corresponding ground-truth images based on the true CRFs of two consumer-level cameras, Nikon 700D and Canon 500D. We demonstrate that the effectively learned CRFs can prevent significant performance drop in three popular HSI-SR models on RGB images from different categories of standard datasets of CAVE, ICVL, Chikuei, Cuprite, Salinas, and KSC. The successfully learned CRF using the method proposed in this study would largely promote a wider implementation of HSI-SR models since tremendous performance drop can be prevented practically." @default.
- W4285292786 created "2022-07-14" @default.
- W4285292786 creator A5006016100 @default.
- W4285292786 creator A5034512737 @default.
- W4285292786 creator A5047578156 @default.
- W4285292786 creator A5082228369 @default.
- W4285292786 date "2022-01-01" @default.
- W4285292786 modified "2023-09-30" @default.
- W4285292786 title "Endmember-Assisted Camera Response Function Learning, Toward Improving Hyperspectral Image Super-Resolution Performance" @default.
- W4285292786 cites W1593447321 @default.
- W4285292786 cites W1704999350 @default.
- W4285292786 cites W1971335521 @default.
- W4285292786 cites W1981883640 @default.
- W4285292786 cites W1984864470 @default.
- W4285292786 cites W1990231296 @default.
- W4285292786 cites W1992426838 @default.
- W4285292786 cites W2010319424 @default.
- W4285292786 cites W2014712350 @default.
- W4285292786 cites W2016956891 @default.
- W4285292786 cites W2021046129 @default.
- W4285292786 cites W2044109926 @default.
- W4285292786 cites W2065623357 @default.
- W4285292786 cites W2072445211 @default.
- W4285292786 cites W2092116045 @default.
- W4285292786 cites W2100109944 @default.
- W4285292786 cites W2123785012 @default.
- W4285292786 cites W2131541495 @default.
- W4285292786 cites W2154761409 @default.
- W4285292786 cites W2162603416 @default.
- W4285292786 cites W2170608472 @default.
- W4285292786 cites W2221899823 @default.
- W4285292786 cites W2290830651 @default.
- W4285292786 cites W2300641502 @default.
- W4285292786 cites W2327302159 @default.
- W4285292786 cites W2462592242 @default.
- W4285292786 cites W2520430674 @default.
- W4285292786 cites W2563097082 @default.
- W4285292786 cites W2604721644 @default.
- W4285292786 cites W2621175972 @default.
- W4285292786 cites W2625894731 @default.
- W4285292786 cites W2752783566 @default.
- W4285292786 cites W2758810255 @default.
- W4285292786 cites W2761454899 @default.
- W4285292786 cites W2766101120 @default.
- W4285292786 cites W2777218179 @default.
- W4285292786 cites W2783018597 @default.
- W4285292786 cites W2790299667 @default.
- W4285292786 cites W2798895617 @default.
- W4285292786 cites W2799767570 @default.
- W4285292786 cites W2807593488 @default.
- W4285292786 cites W2884877436 @default.
- W4285292786 cites W2892288283 @default.
- W4285292786 cites W2893739000 @default.
- W4285292786 cites W2895667927 @default.
- W4285292786 cites W2900702559 @default.
- W4285292786 cites W2910457605 @default.
- W4285292786 cites W2932662143 @default.
- W4285292786 cites W2944248482 @default.
- W4285292786 cites W2953907326 @default.
- W4285292786 cites W2964140612 @default.
- W4285292786 cites W2992641156 @default.
- W4285292786 cites W3005184704 @default.
- W4285292786 cites W3011076973 @default.
- W4285292786 cites W3017506038 @default.
- W4285292786 cites W3023991509 @default.
- W4285292786 cites W3044477028 @default.
- W4285292786 cites W3048175892 @default.
- W4285292786 cites W3093178085 @default.
- W4285292786 cites W3097353710 @default.
- W4285292786 cites W3099239430 @default.
- W4285292786 cites W3102745911 @default.
- W4285292786 cites W4226118325 @default.
- W4285292786 cites W8423413 @default.
- W4285292786 doi "https://doi.org/10.1109/tgrs.2022.3182425" @default.
- W4285292786 hasPublicationYear "2022" @default.
- W4285292786 type Work @default.
- W4285292786 citedByCount "1" @default.
- W4285292786 countsByYear W42852927862023 @default.
- W4285292786 crossrefType "journal-article" @default.
- W4285292786 hasAuthorship W4285292786A5006016100 @default.
- W4285292786 hasAuthorship W4285292786A5034512737 @default.
- W4285292786 hasAuthorship W4285292786A5047578156 @default.
- W4285292786 hasAuthorship W4285292786A5082228369 @default.
- W4285292786 hasBestOaLocation W42852927861 @default.
- W4285292786 hasConcept C146849305 @default.
- W4285292786 hasConcept C153180895 @default.
- W4285292786 hasConcept C154945302 @default.
- W4285292786 hasConcept C159078339 @default.
- W4285292786 hasConcept C205372480 @default.
- W4285292786 hasConcept C205649164 @default.
- W4285292786 hasConcept C31972630 @default.
- W4285292786 hasConcept C41008148 @default.
- W4285292786 hasConcept C62649853 @default.
- W4285292786 hasConcept C82990744 @default.
- W4285292786 hasConceptScore W4285292786C146849305 @default.
- W4285292786 hasConceptScore W4285292786C153180895 @default.
- W4285292786 hasConceptScore W4285292786C154945302 @default.
- W4285292786 hasConceptScore W4285292786C159078339 @default.