Abstract
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.
Abstract (translated)
我们建议通过在高斯过程下对自然光谱建模并将它们与RGB图像组合来从已知光谱量化的RGB图像恢复光谱细节。我们的技术利用过程核来模拟反射光谱的相对平滑度，并鼓励所得信号中的非负性，以更好地估计反射率值。高斯过程是使用空间 - 频谱相关的高光谱训练补丁的集群在集合中推断的。变换每组以匹配测试RGB图像的光谱量化。我们从RGB图像中提取重叠的贴片，并通过对后者进行光谱变换将它们与高光谱训练贴片相匹配。 RGB斑块在与那些高光谱斑块相关的变换高斯过程上编码，并且通过将代码与原始过程组合来构造所得到的图像。我们的方法在受Beta-Bernoulli过程启发的完全贝叶斯模型下推导出所需的高斯过程，我们也为此推出了推理过程。使用三个高光谱数据集的全面评估证明了通过所提出的技术从RGB图像有效提取光谱细节。
URL
https://arxiv.org/abs/1801.04654
