Two-dimensional infrared spectroscopy (2D IR) is a powerful tool to investigate molecular structures and dynamics on femtosecond to picosecond time scales and is applied to diverse systems. Current technologies allow for the acquisition of a single 2D IR spectrum in a few hundreds of milliseconds using a pulse shaper and an array detector, but demanding applications require spectra for many waiting times and involve considerable signal averaging, resulting in data acquisition times that can be many days of laboratory measurement time.
Compressive sampling is an emerging signal processing technique to reduce data acquisition time in diverse fields by requiring only a fraction of the traditional number of measurements while yielding much of the same information as the fully-sampled data.
Here we combine cutting-edge 2D IR methodology with a novel compressive sampling reconstruction algorithm to reduce the data acquisition time of 2D IR spectroscopy without distorting lineshapes. We introduce the Generic Iteratively Reweighted Annihilating Filter (GIRAF) algorithm re-engineered to the specific problem of 2D IR reconstruction and show its effectiveness applied to various systems, including those with low signal, with multiple peaks, and with differing amounts of frequency shifting.
Additionally, we lay the groundwork for 2D IR microscopic imaging using compressive sampling in the spatial image domain. The first instance of a single-pixel camera in the infrared is introduced.