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.

该研究提出一种使用高斯过程和自然光谱相结合的方法，从已知光谱量化的 RGB 图像中恢复光谱细节，通过对具有空间和光谱相关性的高光谱训练补丁聚类，匹配和编码 RGB 图像的补丁来实现，数个高光谱数据集的实验表明该技术对 RGB 图像的光谱细节提取有效。