Compressive Spectral Video via Spectral-Amplitude Coding

Compressive spectral video (CSV) imagers have become a high-interest research topic in the computer vision community. This increasing popularity is due to the CSV imagers allow recovering a four-dimensional tensor from a reduced set of two-dimensional compressed measurements. However, CSV approaches have been limited to compress the spectral dimension for each frame, i.e., temporal compression is not developed. Alternative approaches are the dual-arm systems that rely on a compressive temporal and a spectral imager that fused adjacents measurements to obtain the four-dimensional datacube. Therefore, the development of a CSV imager that allows the reconstruction of the spectral and temporal dimension in a single compressed measurement is highly desired. This work proposes a coded aperture spectral compressive video methodology that allows encoding the spectro-temporal dimensions by using an active binary codification element and a tunable filter. Through the proposed optical system both the spectral and temporal dimensions can be modulated over an integration time using only two optical elements. In order to resolve the inverse problem, an alternating direction multipliers method (ADMM) plug and play (PnP) reconstruction algorithm was modified to render the multidimensional data. Simulations show that the proposed sensing methodology allows spectral video reconstructions from a single compressed measurement. Moreover, simulations show reconstruction results up to 28 dB and 0.86 in PSNR and SSIM metrics, respectively, with compression rates of 99%.