Gas passive infrared imaging technology with high dynamic range (HDR) offers numerous advantages in monitoring harmful gases, including high sensitivity, large detection range, ability to detect multiple hazardous gas types, and strong long-range detection capabilities. However, HDR images cannot be directly displayed on standard devices and need to be compressed to an 8-bit data width for visualization. Improper compression may lead to loss of detail and reduced contrast. Additionally, gas passive infrared imaging often suffers from poor detail, blurred edges, low signal-to-noise ratio, and limited contrast with the background, resulting in overall visual blurring. To address these challenges, this paper proposes an adaptive enhancement and dynamic compression method specifically designed for 14-bit gas infrared images. This method effectively compresses the background information while preserving and enhancing the texture details of weak gas cloud targets. The proposed methodology involves a hierarchical decomposition of the input image into detail and background layers. Subsequently, each layer undergoes separate processing: the detail layer is subjected to enhancement, while the background layer undergoes dynamic range compression. Finally, the enhanced detail layer and compressed background layer are fused to produce an 8-bit image. A comprehensive evaluation is conducted comparing the proposed algorithm with histogram equalization using both subjective visual assessment and objective evaluation metrics. The results demonstrate that the proposed algorithm successfully enhances the clarity of gas cloud targets, improves overall contrast, and effectively suppresses halo artifacts and image noise.
The principle of Gas Identification Method for Long-Wave Infrared Hyperspectral Imaging based FTIR was described in detail. The method of spectral data preprocessing and calibration,radiation transfer mode, algorithm of identifying and concentration retrieving of chemical gas,the detection limit and detection distance were analyzed. The development situation of passive IR remote sensing for chemical gas cloud detection technique was investigated,as well as the key parameters of them.
It is aimed at environment pollution gases remote sensing in wide infrared spectrum, using the technique of long wavelength infrared Fourier transform spectrometer, by extended the detector responsive spectral range, to detect the spectral ‘fingerprint’ characteristic under the certain condition. The paper specifically described experiments in the spectral range 7.0 cm-1~14.5μm (700-1450 cm-1). The application example by used instrument PARES100 is showed, with both the spectral radiance difference and brightness temperature, the 11 kinds industry common chemical and toxic vapors gas were successfully detected, and the approximate concentration that could been measured in terms of concentration and path length times.
Infrared radiomentric calibration is of critical importance for information quantification of remote sensing of environment at infrared spectrum. In the quantitative analysis, the calibration of the measured spectra is very important. LWIR Interferometric Hyperspectral Imager Spectrometer Prototype (CHIPED-1) is developed for studying Radiation Calibration. Two-point linear calibration method is carried out for the spectrometer by using blackbody respectively. Firstly, relative intensity is converted to the absolute radiation lightness of the object. Then, radiation intensity of the object is converted the brightness temperature spectrum by the method of brightness temperature. The result indicated that this method of Radiation Calibration calibration was very good. This calibration method is of significance to the further analysis of atmospheric transmission and the retrieval of the concentration of infrared chemical gas in atmosphere.
The windowing static spectrometer has the advantage of high spectral resolution and high flux. Then combined the spectrometer reconstruction processing algorithms with the new computer technology CUDA, for the large spectral data and the suitable of being processed in parallel lines. Researched the parallel algorithms and programming including the cube data access, restructuring , filtering, mirroring and FFT. The results show that, compared with the traditional spectral reconstruction algorithms, CUDA-based spectral reconstruction has been greatly speeds up the spectral reconstruction.
Imaging spectrometer can gain two-dimensional space image and one-dimensional spectrum at the same time, which shows high utility in color and spectral measurements, the true color image synthesis, military reconnaissance and so on. In order to realize the fast reconstructed processing of the Fourier transform imaging spectrometer data, the paper designed the optimization reconstructed algorithm with OpenMP parallel calculating technology, which was further used for the optimization process for the HyperSpectral Imager of ‘HJ-1’ Chinese satellite. The results show that the method based on multi-core parallel computing technology can control the multi-core CPU hardware resources competently and significantly enhance the calculation of the spectrum reconstruction processing efficiency. If the technology is applied to more cores workstation in parallel computing, it will be possible to complete Fourier transform imaging spectrometer real-time data processing with a single computer.
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