Multispectral Compressive Imaging Strategies using Fabry-Pérot Filtered Sensors
This work addresses multispectral imaging for computational systems, offering practical guidelines but is incremental as it builds on existing compressive sensing and inpainting methods.
The paper tackles multispectral compressive imaging by introducing two device architectures that use Fabry-Pérot filtered sensors without dispersive elements, with the second technique performing best at high compression levels in calibrated setups, while the simpler first technique is favored otherwise.
This paper introduces two acquisition device architectures 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 which integrates narrowband Fabry-Pérot spectral filters at the pixel level. The first scheme leverages joint inpainting and super-resolution to fill in those voxels that are missing due to the device's limited pixel count. The second scheme, in link with compressed sensing, introduces spatial random convolutions, but is more complex and may be affected by diffraction. In both cases we solve the associated inverse problems by using the same signal prior. Specifically, we propose a redundant analysis signal prior in a convex formulation. Through numerical simulations, we explore different realistic setups. Our objective is also to highlight some practical guidelines and discuss their complexity trade-offs to integrate these schemes into actual computational imaging systems. Our conclusion is that the second technique performs best at high compression levels, in a properly sized and calibrated setup. Otherwise, the first, simpler technique should be favored.