Sparse Recovery of Hyperspectral Signal from Natural RGB Images
By Boaz Arad, Ohad Ben-Shahar. European Conference on Computer Vision, 2016
665 citations recorded by the source corpus.
Subfields: Atomic and Molecular Physics, and Optics, Computer Vision and Pattern Recognition, Media Technology
View Paper: https://www.semanticscholar.org/paper/e5238abb6b17d13493e6d2c91e42f0ce8347a990
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Atomic and Molecular Physics, and Optics
In Computer Vision and Pattern Recognition
In Media Technology
Citations in the corpus
- MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction (2022, methodology): “Conventional SR methods [1, 2, 34, 62, 66] are mainly based on hand-crafted hyperspectral priors.”
- HSCNN+: Advanced CNN-Based Hyperspectral Recovery from RGB Images (2018, methodology): “Data Set Sparse Coding [4] HSCNN [39] HSCNN-u HSCNN-R HSCNN-D MRAE / RMSE MRAE / RMSE MRAE / RMSE MRAE / RMSE MRAE / RMSE”