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Compressive imaging using coded apertures (CA) is a powerful technique thatcan be used to recover depth, light fields, hyperspectral images and otherquantities from a single snapshot. The performance of compressive imagingsystems based on CAs mostly depends on two factors: the properties of themask's attenuation pattern, that we refer to as "codification" and thecomputational techniques used to recover the quantity of interest from thecoded snapshot. In this work, we introduce the idea of using time-varying CAssynchronized with spatially varying pixel shutters. We divide the exposure of asensor into sub-exposures at the beginning of which the CA mask changes and atwhich the sensor's pixels are simultaneously and individually switched "on" or"off". This is a practically appealing codification as it does not introduceadditional optical components other than the already present CA but uses achange in the pixel shutter that can be easily realized electronically. We showthat our proposed time multiplexed coded aperture (TMCA) can be optimizedend-to-end and induces better coded snapshots enabling superior reconstructionsin two different applications: compressive light field imaging andhyperspectral imaging. We demonstrate both in simulation and on real captures(taken with prototypes we built) that this codification outperforms thestate-of-the-art compressive imaging systems by more than 4dB in thoseapplications.

Time-Multiplexed Coded Aperture Imaging: Learned Coded Aperture and Pixel Exposures for Compressive Imaging Systems