Entangled photons are distributed over vast distances using a satellite-to-ground link.

We have focused on the optical form that is low cost while maintaining high performance for airborne application. We report the optical design as well as the alignment and test results for a push-broom imaging spectrometer. The smart architecture of the prism-grating based spectrometer ensures high uniformity and image quality. Moreover, an effective method for aligning the spectrometer is also proposed. The results of laboratory-based optical tests and a flight test confirm the easy manufacture and excellent performance. Thus, the proposed system should be suitable for use as a hyperspectral instrument that can be loaded onto airborne and unmanned aerial vehicles.

We discuss and evaluate a long wave infrared imaging spectrometer in terms of its opto-mechanical design and analysis, alignment, testing, and calibration. The instrument is a practical airborne sensor achieving high spectral resolution and sensitive noise equivalent delta temperature. The instrument operates in the 8 to 12.5 μm spectral region with 28.85 nm spectral sampling, 1 mrad instantaneous field of view, and >40° cross track field. The instrument comprises three uniform sub-modules with identical design parameters and performances. The sub-module design is based on a refractive foreoptics feeding an all-reflective spectrometer. The optical form of the spectrometer is a double-pass reflective triplet with a flat grating, which has a fast f/2 and high optical throughput. Cryogenic optics of 100 K is implemented only for the spectrometer. Assembly and thermal deformation and focusing adjustment design are particularly considered for this low temperature. All the mirrors of the spectrometer are opto-mechanical-integrated designed and manufactured by single-point diamond turning technology. We consider the center sub-module as an example, and we present its laboratory testing results and calibration; the results indicate the instrument’s potential value in airborne sensing.

Super resolution methods alleviate the high cost and high difficulty in applying high resolution infrared image sensors. In this paper we present a novel single image super resolution method for infrared images by combining compressive sensing theory and deep learning. Low resolution images can be regarded as the compressed sampling results of the high resolution ones in compressive sensing. With sparsity in this theory, higher resolution images can be reconstructed. However, because of diverse level of sparsity for different images, the output contains noise and loss of high frequency information. Deep convolutional neural network provides a solution to relieve the noise and supplement some missing high frequency information. By concatenating two methods, we manage to produce better results in super resolution tasks for infrared images than SRCNN and ScSR. PSNR and SSIM values are used to quantify the performance. Applying our method to open datasets and actual infrared imaging experiments, we also find better visual results are preserved.