Grayscale to Hyperspectral at Any Resolution Using a Phase-Only Lens

We consider the problem of reconstructing a $H\times W\times 31$ hyperspectral image from a $H\times W$ grayscale snapshot measurement that is captured using a single diffractive optic and a filterless panchromatic photosensor. This problem is severely ill-posed, and we present the first model that is able to produce high-quality results. We train a conditional denoising diffusion model that maps a small grayscale measurement patch to a hyperspectral patch. We then deploy the model to many patches in parallel, using global physics-based guidance to synchronize the patch predictions. Our model can be trained using small hyperspectral datasets and then deployed to reconstruct hyperspectral images of arbitrary size. Also, by drawing multiple samples with different seeds, our model produces useful uncertainty maps. We show that our model achieves state-of-the-art performance on previous snapshot hyperspectral benchmarks where reconstruction is better conditioned. Our work lays the foundation for a new class of high-resolution hyperspectral imagers that are compact and light-efficient.

Polarization Multi-Image Synthesis with Birefringent Metasurfaces

Optical metasurfaces composed of precisely engineered nanostructures have gained significant attention for their ability to manipulate light and implement distinct functionalities based on the properties of the incident field. Computational imaging systems have started harnessing this capability to produce sets of coded measurements that benefit certain tasks when paired with digital post-processing. Inspired by these works, we introduce a new system that uses a birefringent metasurface with a polarizer-mosaicked photosensor to capture four optically-coded measurements in a single exposure. We apply this system to the task of incoherent opto-electronic filtering, where digital spatial-filtering operations are replaced by simpler, per-pixel sums across the four polarization channels, independent of the spatial filter size. In contrast to previous work on incoherent opto-electronic filtering that can realize only one spatial filter, our approach can realize a continuous family of filters from a single capture, with filters being selected from the family by adjusting the post-capture digital summation weights. To find a metasurface that can realize a set of user-specified spatial filters, we introduce a form of gradient descent with a novel regularizer that encourages light efficiency and a high signal-to-noise ratio. We demonstrate several examples in simulation and with fabricated prototypes, including some with spatial filters that have prescribed variations with respect to depth and wavelength.