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An approach is described for spectrally parallel hyperspectral mid-infrared imaging with spatial resolution dictated by fluorescence imaging. Quantum cascade laser (QCL)-based dual-comb mid-infrared spectroscopy enables the acquisition of infrared spectra at high speed (<1 millisecond) through the generation of optical beat patterns and radio-frequency detection. The high-speed nature of the spectral acquisition is shown to support spectral mapping in microscopy measurements. Direct detection of the transmitted infrared beam yields high signal-to-noise spectral information, but long infrared wavelengths impose low diffraction-limited spatial resolution. The use of fluorescence detected photothermal infrared (F-PTIR) imaging provides high spatial resolution tied directly to the integrated IR absorption. Computational imaging using a multi-agent consensus equilibrium (MACE) approach combines the high spatial resolution of F-PTIR and the high spectral information of dual-comb infrared transmission in a single optimized equilibrium hyperspectral data cube. 

While radiography is routinely used to probe complex, evolving density fields in research areas ranging from materials science to shock physics to inertial confinement fusion and other national security applications, complications resulting from noise, scatter, complex beam dynamics, etc. prevent current methods of reconstructing density from being accurate enough to identify the underlying physics with sufficient confidence. In this work, we show that usingonlyfeatures that are robustly identifiable in radiographs and combining them with the underlying hydrodynamic equations of motion using a machine learning approach of a conditional generative adversarial network (cGAN) provides a new and effective approach to determine density fields from a dynamic sequence of radiographs. In particular, we demonstrate the ability of this method to outperform a traditional, direct radiograph to density reconstruction in the presence of scatter, even when relatively small amounts of scatter are present. Our experiments on synthetic data show that the approach can produce high quality, robust reconstructions. We also show that the distance (in feature space) between a testing radiograph and the training set can serve as a diagnostic of the accuracy of the reconstruction.