# NTIRE 2020 Challenge on Spectral Reconstruction from an RGB Image

arXiv: https://arxiv.org/abs/2005.03412 · HTML: https://mlsanity.com/paper/2005.03412/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2020.

## Built on by (most cited first, 30 of 30)

- [MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction](https://mlsanity.com/paper/2204.07908/) (2022)
- [ESSAformer: Efficient Transformer for Hyperspectral Image Super-resolution](https://mlsanity.com/paper/2307.14010/) (2023)
- [Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report](https://mlsanity.com/paper/2210.11153/) (2022)
- [Learning A 3D-CNN and Transformer Prior for Hyperspectral Image Super-Resolution](https://mlsanity.com/paper/2111.13923/) (2021)
- [Deep Amended Gradient Descent for Efficient Spectral Reconstruction from Single RGB Images](https://mlsanity.com/paper/2108.05547/) (2021)
- [Semantic-embedded Unsupervised Spectral Reconstruction from Single RGB Images in the Wild](https://mlsanity.com/paper/2108.06659/) (2021)
- [JR2net: A Joint Non-Linear Representation and Recovery Network for Compressive Spectral Imaging](https://mlsanity.com/paper/2205.07770/) (2022)
- [Limitations of Data-Driven Spectral Reconstruction -- An Optics-Aware Analysis](https://mlsanity.com/paper/2401.03835/) (2024)
- [GMSR:Gradient-Guided Mamba for Spectral Reconstruction from RGB Images](https://mlsanity.com/paper/2405.07777/) (2024)
- [Continuous Spectral Reconstruction from RGB Images via Implicit Neural Representation](https://mlsanity.com/paper/2112.13003/) (2021)
- [HPRN: Holistic Prior-embedded Relation Network for Spectral Super-Resolution](https://mlsanity.com/paper/2112.14608/) (2021)
- [Deep-learning-based Hyperspectral imaging through a RGB camera](https://mlsanity.com/paper/2107.05190/) (2021)
- [Test-time Training for Hyperspectral Image Super-resolution](https://mlsanity.com/paper/2409.08667/) (2024)
- [Hyperspectral Image Super-Resolution in Arbitrary Input-Output Band Settings](https://mlsanity.com/paper/2103.10614/) (2021)
- [D2UF: Deep Coded Aperture Design and Unrolling Algorithm for Compressive Spectral Image Fusion](https://mlsanity.com/paper/2205.12158/) (2022)
- [Mixture-Net: Low-Rank Deep Image Prior Inspired by Mixture Models for Spectral Image Recovery](https://mlsanity.com/paper/2211.02973/) (2022)
- [mHealth hyperspectral learning for instantaneous spatiospectral imaging of hemodynamics](https://mlsanity.com/paper/2303.16205/) (2023)
- [Multispectral Stereo-Image Fusion for 3D Hyperspectral Scene Reconstruction](https://mlsanity.com/paper/2401.09428/) (2023)
- [Comparative Analysis of Hyperspectral Image Reconstruction Using Deep Learning for Agricultural and Biological Applications](https://mlsanity.com/paper/2405.13331/) (2024)
- [HYDRA: HYbrid knowledge Distillation and spectral Reconstruction Algorithm for high channel hyperspectral camera applications](https://mlsanity.com/paper/2510.16664/) (2025)
- [M3SR: Multi-Scale Multi-Perceptual Mamba for Efficient Spectral Reconstruction](https://mlsanity.com/paper/2601.08293/) (2026)
- [Hyperspectral Image Super-Resolution with Spectral Mixup and Heterogeneous Datasets](https://mlsanity.com/paper/2101.07589/) (2021)
- [Learning Exhaustive Correlation for Spectral Super-Resolution: Where Spatial-Spectral Attention Meets Linear Dependence](https://mlsanity.com/paper/2312.12833/) (2023)
- [Efficient and Accurate Hyperspectral Image Demosaicing with Neural Network Architectures](https://mlsanity.com/paper/2403.12050/) (2023)
- [Unleashing Correlation and Continuity for Hyperspectral Reconstruction from RGB Images](https://mlsanity.com/paper/2501.01481/) (2025)
- [NukesFormers: Unpaired Hyperspectral Image Generation with Non-Uniform Domain Alignment](https://mlsanity.com/paper/2503.07004/) (2025)
- [Model-Guided Network with Cluster-Based Operators for Spatio-Spectral Super-Resolution](https://mlsanity.com/paper/2505.24605/) (2025)
- [Modulate and Reconstruct: Learning Hyperspectral Imaging from Misaligned Smartphone Views](https://mlsanity.com/paper/2507.01835/) (2025)
- [A Deep Unfolding Framework for Diffractive Snapshot Spectral Imaging](https://mlsanity.com/paper/2507.04622/) (2025)
- [PHASE: Physiology-Aware Hyperspectral Reconstruction via Object-to-Human Domain Adaptation](https://mlsanity.com/paper/2511.13020/) (2025)

## Abstract (arXiv)

This paper reviews the second challenge on spectral reconstruction from RGB images, i.e., the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB image. As in the previous challenge, two tracks were provided: (i) a "Clean" track where HS images are estimated from noise-free RGBs, the RGB images are themselves calculated numerically using the ground-truth HS images and supplied spectral sensitivity functions (ii) a "Real World" track, simulating capture by an uncalibrated and unknown camera, where the HS images are recovered from noisy JPEG-compressed RGB images. A new, larger-than-ever, natural hyperspectral image data set is presented, containing a total of 510 HS images. The Clean and Real World tracks had 103 and 78 registered participants respectively, with 14 teams competing in the final testing phase. A description of the proposed methods, alongside their challenge scores and an extensive evaluation of top performing methods is also provided. They gauge the state-of-the-art in spectral reconstruction from an RGB image.