# Hyperspectral Demosaicing of Snapshot Camera Images Using Deep Learning

arXiv: https://arxiv.org/abs/2211.15435 · HTML: https://mlsanity.com/paper/2211.15435/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 39 references that are in the map.
- First posted on arXiv 21 Nov 2022.

## Builds on (most cited first, 4 of 4)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network](https://mlsanity.com/paper/1707.05425/) (2017)
- [Perfusion assessment via local remote photoplethysmography (rPPG)](https://mlsanity.com/paper/2208.13840/) (2022)

## Built on by (most cited first, 4 of 4)

- [Unsupervised Spectral Demosaicing with Lightweight Spectral Attention Networks](https://mlsanity.com/paper/2307.01990/) (2023)
- [Multispectral Stereo-Image Fusion for 3D Hyperspectral Scene Reconstruction](https://mlsanity.com/paper/2401.09428/) (2023)
- [Efficient and Accurate Hyperspectral Image Demosaicing with Neural Network Architectures](https://mlsanity.com/paper/2403.12050/) (2023)
- [Generative Model-Assisted Demosaicing for Cross-multispectral Cameras](https://mlsanity.com/paper/2503.02322/) (2025)

## Abstract (arXiv)

Spectral imaging technologies have rapidly evolved during the past decades. The recent development of single-camera-one-shot techniques for hyperspectral imaging allows multiple spectral bands to be captured simultaneously (3x3, 4x4 or 5x5 mosaic), opening up a wide range of applications. Examples include intraoperative imaging, agricultural field inspection and food quality assessment. To capture images across a wide spectrum range, i.e. to achieve high spectral resolution, the sensor design sacrifices spatial resolution. With increasing mosaic size, this effect becomes increasingly detrimental. Furthermore, demosaicing is challenging. Without incorporating edge, shape, and object information during interpolation, chromatic artifacts are likely to appear in the obtained images. Recent approaches use neural networks for demosaicing, enabling direct information extraction from image data. However, obtaining training data for these approaches poses a challenge as well. This work proposes a parallel neural network based demosaicing procedure trained on a new ground truth dataset captured in a controlled environment by a hyperspectral snapshot camera with a 4x4 mosaic pattern. The dataset is a combination of real captured scenes with images from publicly available data adapted to the 4x4 mosaic pattern. To obtain real world ground-truth data, we performed multiple camera captures with 1-pixel shifts in order to compose the entire data cube. Experiments show that the proposed network outperforms state-of-art networks.