Infrared (IR) small target detection with high detection rate, low false alarm rate, and high detection speed has a significant value, but it is usually very difficult since the small targets are usually very dim and may be easily drowned in different types of interferences. Current algorithms cannot effectively enhance real targets and suppress all the types of interferences simultaneously. In this letter, a multiscale detection algorithm utilizing the relative local contrast measure (RLCM) is proposed. It has a simple structure: first, the multiscale RLCM is calculated for each pixel of the raw IR image to enhance real targets and suppress all the types of interferences simultaneously; then, an adaptive threshold is applied to extract real targets. Experimental results show that the proposed algorithm can deal with different sizes of small targets under complex backgrounds and has a better effectiveness and robustness against existing algorithms. Besides, the proposed algorithm has the potential of parallel processing, which is very useful for improving the detection speed.

The Brillouin scattering spectrum has been used to investigate the properties of a liquid medium. Here, we propose an improved method based on the double-edge technique to obtain the Brillouin spectrum of a liquid. We calculated the transmission ratios and deduced the Brillouin shift and linewidth to construct the Brillouin spectrum by extracting the Brillouin edge signal through filtered double-edge data. We built a detection system to test the performance of this method and measured the Brillouin spectrum for distilled water at different temperatures and compared it with the theoretical prediction. The observed difference between the experimental and theoretical values for Brillouin shift and linewidth is less than 4.3 MHz and 3.2 MHz, respectively. Moreover, based on the double-edge technique, the accuracy of the extracted temperatures and salinity is approximately 0.1 °C and 0.5%, respectively, indicating significant potential for application in water detection and oceanography.

A novel high-spectral-resolution lidar receiver based on a Fizeau interferometer and a photomultiplier tube array for tropospheric temperature profiling is introduced. Compared to other temperature lidars, an imaging approach is used to resolve the entire Rayleigh–Brillouin (RB) spectrum without applying frequency scanning techniques. The functionality of the system is demonstrated by means of a nighttime measurement. Atmospheric temperature is retrieved from 4.0 km to 9.2 km by analyzing the measured RB spectra with the Tenti S6 line shape model. The systematic error of the retrieved temperatures is determined to be smaller than 3 K, and the corresponding random error varies between 1.7 K (4.0 km) and 2.3 K (9.2 km) for an observation time of 5 min and a vertical resolution of 0.3 km. Considering the short averaging time and the stable arrangement of the system, the suggested approach is also attractive for future airborne applications.

The Brillouin backscattered signal of LIDAR can be used in remote sensing of ocean parameters such as temperature. Current methods for measuring ocean temperature are based on the monotropic function between Brillouin shift and ocean temperature, while the effect of the ocean salinity, which influences the measurement of temperature, is neglected. However, the variations in salinity will also lead to nonignorable change of the temperature. Neglecting it will result in an inaccurate measurement of temperature. We first propose a new model that takes both Brillouin shift and linewidth as independent variables, and then establish the multi-valued equations of these variables and ocean parameters. With a data fitting procedure the ocean temperature and salinity can be measured simultaneously. The accuracy of this model and the uncertainty of the measurement are analyzed. Theoretical and experimental analysis showed that the ocean temperature and salinity can be simultaneously obtained by measured Brillouin frequency shift and linewidth.

On the basis of experimental Rayleigh-Brillouin scattering data in gaseous nitrogen and air, simulations are performed to describe the observed frequency profiles in analytical form. The experimental data pertain to a λ = 366 nm scattering wavelength, a 90° scattering angle, pressures of 1 and 3 bar, and temperatures in the range 250 – 340 K. Two different models are used to represent the RB-profiles, to distinguish the RB-peaks, and to obtain the Brillouin shift associated with the acoustic waves generated in a gaseous medium. Calculations in the framework of V3 and G3 models, exhibiting composite profiles of three distinct peaks of Voigt or Gaussian functions, are compared to observation. Fitting results show that the V3 model yields an improvement over the G3 model. This mathematical model provides an even better representation of the observed profiles than the Tenti S6 model, which is considered to be the optimum representation in terms of physical parameters. For the derivation of Brillouin shifts, both models perform well at high gas pressure, while at lower pressures, the V3 model yields a higher accuracy than the G3 model.

It is always a challenging task to detect an infrared (IR) small target with high-detection rate, low false alarm rate, and high detection speed since the target usually has a small size and dim gray value, and the background is usually complex. Local contrast, which is based on the human visual system, has been proved efficient for IR small target detection, but existing local contrast algorithms are either difference-form or ratio-form and cannot enhance true target and suppress background simultaneously. In addition, most of them are pixel-level algorithms and require a huge amount of calculations. A simple but efficient method named subblock-level ratio-difference joint local contrast measure (SRDLCM) is proposed; it can enhance real small target and suppress complex background simultaneously. In addition, SRDLCM is calculated for each subblock but not each pixel, so its calculation amount can be reduced significantly. The experimental results on seven real IR sequences and one single-frame image dataset show that the proposed algorithm can achieve a good detection performance in detection rate and false alarm rate with a good robustness, and the time consumed for a single frame is only less than 0.04 s.