Multispectral Compressive Imaging Strategies using Fabry-P\'erot Filtered Sensors

TL;DR

This paper presents two novel multispectral compressive imaging architectures using Fabry-Pérot filters, avoiding dispersive elements, and compares their performance and practical considerations through simulations.

Contribution

Introduces two new sensor architectures for multispectral compressive imaging with pixel-level spectral filtering and analyzes their effectiveness and trade-offs.

Findings

Second technique excels at high compression levels.

First technique is simpler and preferable at lower compression.

Practical guidelines for implementation are discussed.

Abstract

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-P\'erot 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.