We introduce a novel video-rate hyperspectral imager with high spatial, temporal and spectral resolutions. Our key hypothesis is that spectral profiles of pixels within each super-pixel tend to be similar. Hence, a scene-adaptive spatial sampling of a hyperspectral scene, guided by its super-pixel segmented image, is capable of obtaining high-quality reconstructions. To achieve this, we acquire an RGB image of the scene, compute its super-pixels, from which we generate a spatial mask of locations where we measure high-resolution spectrum. The hyperspectral image is subsequently estimated by fusing the RGB image and the spectral measurements using a learnable guided filtering approach. Due to low computational complexity of the superpixel estimation step, our setup can capture hyperspectral images of the scenes with little overhead over traditional snapshot hyperspectral cameras, but with significantly higher spatial and spectral resolutions. We validate the proposed technique with extensive simulations as well as a lab prototype that measures hyperspectral video at a spatial resolution of 600 × 900 pixels, at a spectral resolution of 10 nm over visible wavebands, and achieving a frame rate at 18fps.

中文翻译：

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SASSI™ 超像素化自适应空间光谱成像


我们推出了一种具有高空间、时间和光谱分辨率的新型视频速率高光谱成像仪。我们的关键假设是每个超像素内像素的光谱轮廓往往相似。因此，在超像素分割图像的引导下，对高光谱场景进行场景自适应空间采样，能够获得高质量的重建。为了实现这一目标，我们获取场景的 RGB 图像，计算其超像素，从中生成测量高分辨率光谱的位置的空间掩模。随后使用可学习的引导滤波方法融合 RGB 图像和光谱测量来估计高光谱图像。由于超像素估计步骤的计算复杂度较低，我们的设置可以捕获场景的高光谱图像，与传统快照高光谱相机相比，开销很小，但具有明显更高的空间和光谱分辨率。我们通过广泛的模拟以及实验室原型验证了所提出的技术，该原型以 600 × 900 像素的空间分辨率、在可见光波段的光谱分辨率为 10 nm 测量高光谱视频，并实现 18fps 的帧速率。