SASSI -- Super-Pixelated Adaptive Spatio-Spectral Imaging
This work addresses the problem of capturing high-resolution hyperspectral video for applications requiring detailed spectral and temporal information, offering a significant improvement over traditional snapshot hyperspectral cameras.
This paper introduces a novel video-rate hyperspectral imager called SASSI that leverages super-pixel segmentation to guide scene-adaptive spatial sampling. The system achieves high-quality hyperspectral reconstructions at a spatial resolution of 600x900 pixels, a spectral resolution of 10 nm, and a frame rate of 18fps.
We introduce a novel video-rate hyperspectral imager with high spatial, and temporal resolutions. Our key hypothesis is that spectral profiles of pixels in a super-pixel of an oversegmented image tend to be very similar. Hence, a scene-adaptive spatial sampling of an 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 $600 \times 900$ pixels, at a spectral resolution of 10 nm over visible wavebands, and achieving a frame rate at $18$fps.