“Random Convolution”

Multispectral Compressive Imaging Strategies Using Fabry–Pérot Filtered Sensors

Abstract: In this paper, we introduce two novel acquisition schemes for multispectral compressive imaging. Unlike most existing methods, the proposed computational imaging techniques do not include any dispersive element, as they use a dedicated sensor that integrates narrowband Fabry–Pérot spectral filters at the pixel level.

A (256*256) pixel 76.7mW CMOS imager/ compressor based on real-time in-pixel compressive sensing:

Abstract: A CMOS imager is presented which has the ability to perform localized compressive sensing on-chip. In-pixel convolutions of the sensed image with measurement matrices are computed in real time, and a proposed programmable two-dimensional scrambling technique guarantees the randomness of the coefficients used in successive observation.

CMOS Compressed Imaging by Random Convolution