Hyperspectral Recovery from RGB Images using Gaussian Processes.

Area of Science:

• Computer Vision
• Computational Imaging
• Spectral Imaging

Background:

• Recovering detailed spectral information from standard RGB images is challenging due to spectral quantization.
• Natural spectra exhibit relative smoothness, a property that can be modeled using advanced statistical techniques.

Purpose of the Study:

• To develop a novel technique for recovering spectral details from RGB images with known spectral quantization.
• To leverage Gaussian Processes for modeling natural spectra and integrating them with RGB image data.

Main Methods:

• Modeling natural spectra using Gaussian Processes with Process Kernels to capture reflectance smoothness.
• Inferring Gaussian Processes from spatio-spectrally correlated hyperspectral training patches.
• Transforming hyperspectral patches to match the target RGB image's spectral quantization.
• Encoding RGB image patches onto transformed Gaussian Processes and reconstructing the spectral details.

Main Results:

• Demonstrated effective extraction of spectral details from RGB images.
• Successfully inferred Gaussian Processes under a fully Bayesian model inspired by the Beta-Bernoulli Process.
• Validated the technique's performance across three diverse hyperspectral datasets.

Conclusions:

• The proposed method offers a robust approach to spectral detail recovery from RGB images.
• Gaussian Process modeling provides a powerful framework for enhancing spectral information in standard imaging.
• This technique has significant implications for applications requiring detailed spectral analysis from readily available RGB data.