Hyperspectral (HS) cameras record the spectrum at multiple wavelengths for each pixel in an image, and are used, e.g., for quality control and agricultural remote sensing. We introduce a fast, cost-efficient and mobile method of taking HS images using a regular digital camera equipped with a passive diffraction grating filter, using machine learning for constructing the HS image. The grating distorts the image by effectively mapping the spectral information into spatial dislocations, which we convert into a HS image by a convolutional neural network utilizing novel wide dilation convolutions that accurately model optical properties of diffraction. We demonstrate high-quality HS reconstruction using a model trained on only 271 pairs of diffraction grating and ground truth HS images.

高光谱（HS）相机记录图像中每个像素在多个波长处的光谱，并用于例如质量控制和农业遥感。我们介绍了一种使用配备有无源衍射光栅滤镜的常规数码相机，通过机器学习来构造HS图像的快速，经济高效的移动方法来拍摄HS图像。光栅通过将光谱信息有效地映射到空间位错而使图像失真，然后我们通过卷积神经网络利用新颖的宽扩散卷积将卷积神经网络转换为HS图像，从而精确地模拟了衍射的光学特性。我们展示了使用仅对271对衍射光栅和地面真相HS图像训练的模型进行的高质量HS重建。