# Hyperspectral Reconstruction of Skin Through Fusion of Scattering Transform Features

arXiv: https://arxiv.org/abs/2404.10030 · HTML: https://mlsanity.com/paper/2404.10030/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 9 references that are in the map.
- First posted on arXiv 15 Apr 2024.

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

- [MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction](https://mlsanity.com/paper/2204.07908/) (2022)
- [Kymatio: Scattering Transforms in Python](https://mlsanity.com/paper/1812.11214/) (2018)
- [Hyper-Skin: A Hyperspectral Dataset for Reconstructing Facial Skin-Spectra from RGB Images](https://mlsanity.com/paper/2310.17911/) (2023)
- [Generative networks as inverse problems with Scattering transforms](https://mlsanity.com/paper/1805.06621/) (2018)

## Abstract (arXiv)

Hyperspectral imagery (HSI) is an established technique with an array of applications, but its use is limited due to both practical and technical issues associated with spectral devices. The goal of the ICASSP 2024 'Hyper-Skin' Challenge is to extract skin HSI from matching RGB images and an infrared band. To address this problem we propose a model using features of the scattering transform - a type of convolutional neural network with predefined filters. Our model matches and inverts those features, rather than the pixel values, reducing the complexity of matching while grouping similar features together, resulting in an improved learning process.