| Texto completo | |
| Autor(es): |
Silveira, Ana C. Caznok
;
do Carmo, Diedre S.
;
Ueda, Lucas H.
;
Fantinato, Denis G.
;
Costa, Paula D. P.
;
Rittner, Leticia
Número total de Autores: 6
|
| Tipo de documento: | Artigo Científico |
| Fonte: | IEEE OPEN JOURNAL OF SIGNAL PROCESSING; v. 6, p. 7-pg., 2025-01-01. |
| Resumo | |
Hyperspectralchannel reconstruction transforms a subsampled multispectral image into hyperspectral imaging, providing higher spectral resolution without a dedicated acquisition hardware and camera. Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction (MST++) is a state-of-the-art channel reconstruction technique, but it faces memory limitations for high spatial-resolution images. In this context, we introduced VITMST++, a novel architecture incorporating Vision Transformer embeddings for spatial compression, multi-resolution image context, and a custom channel-weighted loss. Developed for the ICASSP 2024 HyperSkin Challenge, VITMST++ outperforms the state-of-the-art MST++ in both performance and computational efficiency in channel reconstruction. In this work, we perform a deeper analysis on the main aspects of VITMST++ efficiency, quantitative performance, and generalization to other datasets. Results show that VITMST++ achieves similar values of SAM and SSIM hyperspectral reconstruction metrics when compared to state-of-the-art methods, while consuming up to three fold less memory and needing up to 10 times fewer multiply-add operations. (AU) | |
| Processo FAPESP: | 20/09838-0 - BI0S - Brazilian Institute of Data Science |
| Beneficiário: | João Marcos Travassos Romano |
| Modalidade de apoio: | Auxílio à Pesquisa - Programa Centros de Pesquisa Aplicada |