Gas cloud imaging with uncooled infrared spectroscopy is influenced by ambient temperature, complicating the quantitative detection of gas concentrations in open environments. To solve the aforementioned challenges, the paper analyzes the main factors influencing detection errors in uncooled infrared spectroscopy gas cloud imaging and proposes a temperature correction method to address them. Firstly, to mitigate the environmental effects on the radiative temperature output of uncooled infrared detectors, a snapshot-based, multi-band infrared temperature compensation algorithm incorporating environmental awareness was developed. This algorithm enables precise infrared radiation prediction across a wide operating temperature range. Validation tests conducted over the full temperature range of 0 °C to 80 °C demonstrated that the prediction error was maintained within ±0.96 °C. Subsequently, temperature compensation techniques were integrated, resulting in the development of a comprehensive uncooled infrared spectroscopy gas cloud imaging detection method. Ultimately, the detection limits for SF6, ethylene, cyclohexane, and ammonia were enhanced by 50%, 33%, 25%, and 67%, respectively.

We propose a real-time hyperspectral video acquisition system that uses coded slits. Conventional imaging spectrometers usually have scanning mechanisms that reduce the temporal resolution or sacrifice the spatial resolution to acquire spectral information instantly. Recently, computational spectral imaging has been applied to realize high-speed or high-performance spectral imaging. However, the most current computational spectral imaging systems take a long time to reconstruct spectral data cubes from limited measurements, which limits real-time hyperspectral video acquisition. In this work, we propose a new computational spectral imaging system. We substitute the slit in a conventional scanning-based imaging spectrometer with coded slits, which can achieve the parallel acquisition of spectral data and thus an imaging speed that is several times higher. We also apply an electronically controlled translation stage to use different codes at each exposure level. The larger amount of data allows for fast reconstruction through matrix inversion. To solve the problem of a trade-off between imaging speed and image quality in high-speed spectral imaging, we analyze the noise in the system. The severe readout noise in our system is suppressed with S-matrix coding. Finally, we build a practical prototype that can acquire hyperspectral video with a high spatial resolution and a high signal-to-noise ratio at 5 Hz in real time.

Herein, we propose a system for a snapshot video hyperspectral imaging method based on a uniformly distributed-slit array (UDA) coding plate that not only effectively improves the scanning speed of spectrometers but also achieves a high spectral fidelity of snapshot videos. A mathematical model and optical link simulation of the new system are established. The analysis results show that the proposed method can more efficiently collect information and restore the spectral data cube, and the spectral smile of the system is less than 4.86 μm. The results of the spectral performance and external imaging tests of the system show that the system has the ability to collect spatial spectrum video information with a frame rate of 10 Hz and identify dynamic targets, laying a foundation for the design of a system with a higher frame rate and resolution.

Digital time-delay integration (TDI) enhances the signal-to-noise ratio (SNR) in infrared (IR) imaging, but its effectiveness in push-broom scanning is contingent upon maintaining a stable image shift velocity. Unpredictable image shifts and rotations, caused by carrier or scene movement, can affect the imaging process. This paper proposes an advanced technical approach for infrared multispectral TDI imaging. This methodology concurrently estimates the image shift and rotation between frames by utilizing a high-resolution visible camera aligned parallel to the optical axis of the IR camera. Subsequently, parameter prediction is conducted using the Kalman model, and real-time compensation is achieved by dynamically adjusting the infrared TDI integration unit based on the predicted parameters. Simulation and experimental results demonstrate that the proposed algorithm enhances the BRISQUE score of the TDI images by 21.37%, thereby validating its efficacy in push-scan imaging systems characterized by velocity-height ratios instability and varying camera attitudes. This research constitutes a significant contribution to the advancement of high-precision real-time compensation for image shift and rotation in infrared remote sensing and industrial inspection applications.