Jiakun Li

Simulations and experiments on resonantly-pumped single-frequency Erbium lasers at 1.6 μm

We report on a single-frequency laser oscillator based on a new Er:YLuAG laser crystal which is spectrally suitable for application as a transmitter in differential absorption lidar measurements of atmospheric CH4. The laser emits singlefrequency laser pulses with 2.3 mJ of energy and 90 ns duration at a repetition rate of 100 Hz. It is resonantly pumped by two linearly polarized single-mode cw fiber lasers at 1532 nm. A scan of the CH4-absorption line at 1645.1 nm was performed and the shape of the line with its substructure was reproduced as theoretically predicted. A 2.5-dimensional performance model was developed, in which pump absorption saturation and laser reabsorption is included. Also the spectral output of the laser oscillator longitudinal multimode operation could be predicted by the laser model.

Gas cloud infrared image enhancement based on anisotropic diffusion

Leakage of dangerous gases will not only pollute the environment, but also seriously threat public safety. Thermal infrared imaging has been proved to be an efficient method to qualitatively detect the gas leakage. But some problems are remained, especially when monitoring the leakage in a passive way. For example, the signal is weak and the edge of gas cloud in the infrared image is not obvious enough. However, we notice some important characteristics of the gas plume and therefore propose a gas cloud infrared image enhancement method based on anisotropic diffusion. As the gas plume presents a large gas cloud in the image and the gray value is even inside the cloud, strong forward diffusion will be used to reduce the noise and to expand the range of the gas cloud. Frames subtraction and K-means cluttering pop out the gas cloud area. Forward-and-Backward diffusion is to protect background details. Additionally, the best iteration times and the time step parameters are researched. Results show that the gas cloud can be marked correctly and enhanced by black or false color, and so potentially increase the possibility of gas leakage detection.

200-W Tm:YLF INNOSLAB laser

A Tm:YLF laser in INNOSLAB design is reported. It produces 200 W of output power at an optical efficiency of 24 % and a slope efficiency of 27 % with respect to incident pump power. The laser crystal is partially end-pumped in a tophat line focus with a width of 12 mm and a height of about 1 mm. It is placed in a stable, spherical laser resonator, which results in a highly elliptical output beam. The beam is near diffraction limit and Gaussian in shape in one axis and contains very high order transversal modes and is Top-Hat-like in shape in the other axis. The beam shape is ideal for pumping a Ho:YLF laser crystal in INNOSLAB design for a pulsed amplifier.

Spatial concentration distribution model for short-range continuous gas leakage of small amount

Passive infrared gas imaging systems have been utilized in the equipment leak detection and repair in chemical manufacturers and petroleum refineries. The detection performance mainly relates to the sensitivity of infrared detector, optical depth of gas, atmospheric transmission, wind speed, and so on. Based on our knowledge, the spatial concentration distribution of continuously leaking gas plays an important part in leak detection. Several computational model of gas diffusion were proposed by researchers, such as Gaussian model, BM model, Sutton model and FEM3 model. But these models focus on calculating a large scale gas concentration distribution for a great amount of gas leaks above over 100- meter height, and not applicable to assess detection limit of a gas imaging system in short range. In this paper, a wind tunnel experiment is designed. Under different leaking rate and wind speed, concentration in different spatial positions is measured by portable gas detectors. Through analyzing the experimental data, the two parameters σy(x) and σz (x) that determine the plume dispersion in Gaussian model are adjusted to produce the best curve fit to the gas concentration data. Then a concentration distribution model for small mount gas leakage in short range is established. Various gases, ethylene and methane are used to testify this model.

Gas imaging detectivity model combining leakage spot size and range

As to visualize the leaking gas cloud which is not visible to the naked eyes, three categories of techniques have emerged, Backscatter Absorption Gas Imaging, Passive Thermal Imaging, and Imaging Spectrometer. Among these systems, Signal to Noise Ratio (SNR) is generally used to deduce gas leakage detection limit and leads to several performance evaluation parameters, such as Noise-Equivalent Spectral Radiance and Noise-Equivalent Concentration-Path Length. However, in most cases, measuring the SNR accurately is not accessible and usually needs auxiliary instruments. Therefore, we focus on researching a gas leakage detection model according to the general parameter of a thermal imager, Noise Equivalent Temperature Difference (NETD). Firstly, the Gas Equivalent Blackbody Temperature Difference (GEBTD) is obtained by calculating the attenuated radiation of the On-plume path and that of the Off-plume path respectively. A simplified form of GEBTD was derived by our previous paper, assuming that the work range was short and the affection of atmospheric transmission was omitted. But in this paper, more factors are considered to establish a more realistic and accurate detectivity model. The radiation of the gas cloud and the attenuation of the atmosphere are taken into account as well as the size of the leakage spot which inevitably affects the concentration path length. Secondly, the NETD and the GEBTD are compared to determine the detection capability. At last, an experiment is designed to verify the accuracy and reliability of this model on the basis of the gas cloud concentration cone distribution model.

Gas image enhancement based on adaptive time-domain filtering and morphology

The fingerprint region of most gases is within 3 to 14μm. A mid-wave or long-wave infrared thermal imager is therefore commonly applied in gas detection. With further influence of low gas concentration and heterogeneity of infrared focal plane arrays, the image has numerous drawbacks. These include loud noise, weak gas signal, gridding, and dead points, all of which are particularly evident in sequential images. In order to solve these problems, we take into account the characteristics of the leaking gas image and propose an enhancement method based on adaptive time-domain filtering with morphology. The adaptive time-domain filtering which operates on time sequence images is a hybrid method combining the recursive filtering and mean filtering. It segments gas and background according to a selected threshold; removes speckle noise according to the median; and removes background domain using weighted difference image. The morphology method can not only dilate the gas region along the direction of gas diffusion to greatly enhance the visibility of the leakage area, but also effectively remove the noise, and smooth the contour. Finally, the false color is added to the gas domain. Results show that the gas infrared region is effectively enhanced.

A grayscale image color transfer method based on region texture analysis using GLCM

In order to improve the performance of grayscale image colorization based on color transfer, this paper proposes a novel method by which pixels are matched accurately between images through region texture analysis using Gray Level Co-occurrence Matrix (GLCM). This method consists of six steps: reference image selection, color space transformation, grayscale linear transformation and compression, texture analysis using GLCM, pixel matching through texture value comparison, and color value transfer between pixels. We applied this method to kinds of grayscale images, and they gained natural color appearance like the reference images. Experimental results proved that this method is more effective than conventional method in accurately transferring color to grayscale images.