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.

中文翻译：

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使用高斯过程从RGB图像中进行高光谱恢复。

我们建议通过在高斯过程下对自然光谱建模并将其与RGB图像结合，从已知光谱量化的RGB图像中恢复光谱细节。我们的技术利用过程核对反射光谱的相对平滑度建模，并鼓励在所得信号中实现非负性，以更好地估计反射率值。使用空间光谱相关的高光谱训练斑块的聚类来推断高斯过程。每组都进行转换以匹配测试RGB图像的光谱量化。我们从RGB图像中提取重叠的色块，并通过对后者进行光谱变换，将它们与高光谱的训练色块进行匹配。RGB斑块在与那些高光谱斑块相关的变换高斯过程上进行编码，并且通过将代码与原始过程组合来构造所得图像。在贝塔-贝努利过程的启发下，我们的方法在完全贝叶斯模型下推断出所需的高斯过程，为此，我们还介绍了推断过程。使用三个高光谱数据集进行的全面评估表明，通过所提出的技术可以有效地从RGB图像中提取光谱细节。