Mid-Wave Infrared Snapshot Compressive Spectral Imager with Deep Infrared Denoising Prior

Although various infrared imaging spectrometers have been studied, most of them are developed under the Nyquist sampling theorem, which severely burdens 3D data acquisition, storage, transmission, and processing, in terms of both hardware and software. Recently, computational imaging, which avoids direct imaging, has been investigated for its potential in the visible field. However, it has been rarely studied in the infrared domain, as it suffers from inconsistency in spectral response and reconstruction. To address this, we propose a novel mid-wave infrared snapshot compressive spectral imager (MWIR-SCSI). This design scheme provides a high degree of randomness in the measurement projection, which is more conducive to the reconstruction of image information and makes spectral correction implementable. Furthermore, leveraging the explainability of model-based algorithms and the high efficiency of deep learning algorithms, we designed a deep infrared denoising prior plug-in for the optimization algorithm to perform in terms of both imaging quality and reconstruction speed. The system calibration obtains 111 real coded masks, filling the gap between theory and practice. Experimental results on simulation datasets and real infrared scenarios prove the efficacy of the designed deep infrared denoising prior plug-in and the proposed acquisition architecture that acquires mid-infrared spectral images of 640 pixels x 512 pixels x 111 spectral channels at an acquisition frame rate of 50 fps.