# Hyperspectral recovery from RGB images using Gaussian Processes

arXiv: https://arxiv.org/abs/1801.04654 · HTML: https://mlsanity.com/paper/1801.04654/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2018.

## Built on by (most cited first, 13 of 13)

- [Advances in adversarial attacks and defenses in computer vision: A survey](https://mlsanity.com/paper/2108.00401/) (2021)
- [A survey on computational spectral reconstruction methods from RGB to hyperspectral imaging](https://mlsanity.com/paper/2106.15944/) (2021)
- [Hyperspectral Image Reconstruction via Combinatorial Embedding of Cross-Channel Spatio-Spectral Clues](https://mlsanity.com/paper/2312.11119/) (2023)
- [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)
- [Learning to Recover Spectral Reflectance from RGB Images](https://mlsanity.com/paper/2304.02162/) (2023)
- [Multispectral to Hyperspectral using Pretrained Foundational model](https://mlsanity.com/paper/2502.19451/) (2025)
- [FRN: Fractal-Based Recursive Spectral Reconstruction Network](https://mlsanity.com/paper/2505.15439/) (2025)
- [Leveraging pretrained RGB denoisers for hyperspectral image restoration](https://mlsanity.com/paper/2605.24769/) (2026)
- [GLMHA A Guided Low-rank Multi-Head Self-Attention for Efficient Image Restoration and Spectral Reconstruction](https://mlsanity.com/paper/2410.00380/) (2024)
- [RGB Pre-Training Enhanced Unobservable Feature Latent Diffusion Model for Spectral Reconstruction](https://mlsanity.com/paper/2507.12967/) (2025)
- [Uncertainty Quantification in HSI Reconstruction using Physics-Aware Diffusion Priors and Optics-Encoded Measurements](https://mlsanity.com/paper/2511.18473/) (2025)
- [Physics-Guided Deep Unfolding for Blind Cross-Sensor Spectral Super-Resolution via Learning the Spectral Transformation Function](https://mlsanity.com/paper/2606.05759/) (2026)

## Abstract (arXiv)

We propose to recover spectral details from RGB images of known spectral quantization by modeling natural spectra under Gaussian Processes and combining them with the RGB images. Our technique exploits Process Kernels to model the relative smoothness of reflectance spectra, and encourages non-negativity in the resulting signals for better estimation of the reflectance values. The Gaussian Processes are inferred in sets using clusters of spatio-spectrally correlated hyperspectral training patches. Each set is transformed to match the spectral quantization of the test RGB image. We extract overlapping patches from the RGB image and match them to the hyperspectral training patches by spectrally transforming the latter. The RGB patches are encoded over the transformed Gaussian Processes related to those hyperspectral patches and the resulting image is constructed by combining the codes with the original Processes. Our approach infers the desired Gaussian Processes under a fully Bayesian model inspired by Beta-Bernoulli Process, for which we also present the inference procedure. A thorough evaluation using three hyperspectral datasets demonstrates the effective extraction of spectral details from RGB images by the proposed technique.