# Deep-learning-based Hyperspectral imaging through a RGB camera

arXiv: https://arxiv.org/abs/2107.05190 · HTML: https://mlsanity.com/paper/2107.05190/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 41 references that are in the map.
- First posted on arXiv 12 Jul 2021.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks](https://mlsanity.com/paper/1910.03151/) (2019)
- [Gradient Centralization: A New Optimization Technique for Deep Neural Networks](https://mlsanity.com/paper/2004.01461/) (2020)
- [NTIRE 2020 Challenge on Spectral Reconstruction from an RGB Image](https://mlsanity.com/paper/2005.03412/) (2020)
- [AdaptiveWeighted Attention Network with Camera Spectral Sensitivity Prior for Spectral Reconstruction from RGB Images](https://mlsanity.com/paper/2005.09305/) (2020)
- [Compressive Hyperspectral Imaging with Side Information](https://mlsanity.com/paper/1502.06260/) (2015)
- [Accurate Spectral Super-resolution from Single RGB Image Using Multi-scale CNN](https://mlsanity.com/paper/1806.03575/) (2018)
- [Snapshot fiber spectral imaging using speckle correlations and compressive sensing](https://mlsanity.com/paper/1810.10871/) (2018)

## Built on by (most cited first, 2 of 2)

- [Analyzing mixed construction and demolition waste in material recovery facilities: evolution, challenges, and applications of computer vision and deep learning](https://mlsanity.com/paper/2409.13112/) (2024)
- [Uncertainty Quantification in HSI Reconstruction using Physics-Aware Diffusion Priors and Optics-Encoded Measurements](https://mlsanity.com/paper/2511.18473/) (2025)

## Abstract (arXiv)

Hyperspectral image (HSI) contains both spatial pattern and spectral information which has been widely used in food safety, remote sensing, and medical detection. However, the acquisition of hyperspectral images is usually costly due to the complicated apparatus for the acquisition of optical spectrum. Recently, it has been reported that HSI can be reconstructed from single RGB image using convolution neural network (CNN) algorithms. Compared with the traditional hyperspectral cameras, the method based on CNN algorithms is simple, portable and low cost. In this study, we focused on the influence of the RGB camera spectral sensitivity (CSS) on the HSI. A Xenon lamp incorporated with a monochromator were used as the standard light source to calibrate the CSS. And the experimental results show that the CSS plays a significant role in the reconstruction accuracy of an HSI. In addition, we proposed a new HSI reconstruction network where the dimensional structure of the original hyperspectral datacube was modified by 3D matrix transpose to improve the reconstruction accuracy.