Lianfa Bai

Influencing factors of microscanning performance based on flat optical component

To decrease the performance difference between the actual microscanning thermal imager and the theoretical value, a germanium lens (placed at a certain angle between the infrared focal plane array and infrared lens) dip angle model of flat optical component microscanning is introduced in this letter. The model is the basis for choosing the dip angle of the germanium lens, which is used in the microscanning thermal imager. In addition, the actual dip angle of the germanium lens is chosen according to the model, the infrared lens parameters of the thermal imager, and the germanium lens parameters of manufacture and installation. Only in this manner can the optimal performance of the microscanning thermal imager based on the flat optical component be obtained. Results of the experiments confirm the accuracy of the conclusions above.

Analysis and study on the stadia of low light level imaging system

The stadia is an important index of low light level imaging system. It indicates the combination detection property of low light level imaging system. In this paper, taking the low light level night vision system, which is composed of objective, image intensifier and eye lens, as the object of study, experiment and analysis on the stadia of low light level imaging system are carried out in detail. First the stadia theory analysis model of low light level night vision system is described. And then all kinds of factors, which influence the stadia of low light level imaging system, are discussed and analyzed. These factors include the observation effect grade and object equivalent optical transform, 3-dimension Dfτequal stadia chart, MTF, equivalent spatial frequency interval, contrast deterioration coefficient, snow-flower noise and noise factor. At last the measures and advices, which are used to improve the stadia of low light level night vision system, are proposed. The research results of this paper have important meaning on the design, evaluating and manufacturing of new low light level night vision system.

New technique for infrared thermal image processing combining time domain and space domain

In this paper, taking the Digital Infrared Thermal Imager as the object of study, infrared thermal image and its noise types and characteristics are researched into in detail, and the image noise theoretical model is described unitedly by the time and space domain stochastic process. On the basis of these, Infrared Thermal Image Processing System is set up. New types of time and space domain image processing theory and technique-interframe comparison denoise process and adaptive mode filter are put forward and taken to suppress infrared thermal image noise. Theoretical and experimental results show that interframe comparison denoise process and adaptive mode filter suppress infrared thermal image noise more effectively than image frame-accumulation method and mode filter. At last, Infrared Thermal Imager Signal and Noise Test and Analysis System is developed specially, and the test and analysis evaluation on the original and processed images are carried out.

Improved particle filtering algorithm based on the multi-feature fusion for small IR target tracking

A Mean-shift Particle filtering tracking algorithm based on the multi-feature fusion has been raised in this paper. This algorithm mainly focus on the features of the high frequency histogram, fractal and the energy of the infrared small target, which directly against the defects exist in detecting the infrared small targets, such as the size of the target, the low tracking accuracy caused by the low SNR and so on. Since the particle filtering algorithm gives the advantage of multi-feature fusion, the algorithm raised in this paper combines the three features listed above and does the calculation using the particle weight to greatly improved the tracking accuracy. The clustering effect of the Mean-shift algorithm has also been applied to make the distribution of the particles more equals to the real target, which reduced the number of the particle and enhanced the real-time ability of the algorithm. The experimental results show that, this algorithm has better tracking accuracy, which gives more effectiveness in tracking the infrared small target compared to the traditional particle filtering algorithm.

Study on Real-time Registration in Dual Spectrum Low Level Light Night Vision Technique

In low level light (LLL) color night vision technology, dual spectrum images with respective special information were acquired, and target identification probability would be effectively improved through dual spectrum image fusion. Image registration is one of the key technologies during this process. Current dual spectrum image registration methods mainly include dual imaging channel common optical axis scheme and image characteristic pixel searching scheme. In dual imaging channel common optical axis scheme, additional prismatic optical components should be used, and large amount of radiative energy was wasted. In image characteristic pixel searching scheme, complicated arithmetic made it difficult for its real time realization. In this paper, dual channel dual spectrum LLL color night vision system structure feature and dual spectrum image characteristics was studied, dual spectrum image gray scale symbiotic matrix 2-dimensional histogram was analysed, and a real time image registration method including electronic digital shifting, pixel extension and extraction was put forward. By the analysis of spatial gray-scale relativity of fusion image, registration precision is quantitatively expressed. Emulation experiments indicate that this arithmetic is fast and exact for our dual channel dual spectrum image registration. This method was realized on dual spectrum LLL color night vision experimental apparatus based on Texas Instruments digital video processing device DM642.

Mode filter: a new noise suppression method for low-light-level image

In this paper, on the basis of the analysis of the main noise sources of the `Low Light Level (LLL) CCD Camera which our teaching and research section developed, a new method, mode filter, is put forward and taken to suppress the LLL image noise. At last the detailed analysis and discussion on the processing results are given.

Moving target detection method based on polarization characteristics under the condition of moving detector

Moving target detection is a important field of image processing. Because the existing algorithms are vulnerable to background disturbance’s interference in the case of moving detector, a moving target detection algorithm based on target’s polarization characteristics under the condition of the moving detector has been proposed in this paper. Firstly, for the problem of the target detection such as the movement of background and parallax which are caused by the moving detector, a moving target detection method under the condition of moving detector has been proposed. The method gets detector’s motion estimation and compensation parameters by using image feature points matching method, and uses background updating method to achieve target’s detection. Then LK optical flow method is used to get target’s movement information and detector’s movement information and model the target’s and background’s movement information. Eventually this method calculates the relevance of the background’s and target’s movement information model to achieve target detection. Secondly, for the moving target detection method could not solve the problem of Background disturbance which interferes the detection result, a target detection method fused target polarization characteristics has been proposed on the basis of moving target detection method under the conditions of the rotation detector. This method realizes the target detection algorithm based on target’s polarization characteristics under the condition of moving detector, by pre-processing the polarization images to solve the parallax’s effect, clustering and segmenting the pretreated polarization image to extract polarized target, and fusing the moving target detection method. The experiment result shows that this method can effectively detect moving targets with a strong polarization characteristics in the scene, while suppressing the interference brought by strong polarized but still region and weak polarized background disturbance in the sense.

Adaptive infrared-image details enhancement technology

In order to surmount the infrared-image object differentiation difficulty caused by the blurred image edge, a kind of adaptive filter based infrared-image nonlinear edge enhancement technology was proposed in this paper. This technology integrates image nonlinear edge-sharpening and Multi-scale analyze method. The approach of Gauss pyramid structure can enhance detail information by using non-linear algorithms in different scales. The enhanced detail information is then added back to the original image iteratively. While saving the image edge information it can filter image noise and edge distortion caused by edge-sharpening and improve image’s clarity and SNR obviously. Gray scale grads was defined based on gray linear increment, image edge enhancement arithmetic can be real time realized, and has been applied in high performance thermal imager. As it is shown in experiments, this algorithm has practicality and potential application value in the field of infrared images contrast enhancement

Color night vision method based on the correlation between natural color and dual band night image

Color night vision technology can effectively improve the detection and identification probability. Current color night vision method based on gray scale modulation fusion, spectrum field fusion, special component fusion and world famous NRL method, TNO method will bring about serious color distortion, and the observers will be visual tired after long time observation. Alexander Toet of TNO Human Factors presents a method to fuse multiband night image a natural day time color appearance, but it need the true color image of the scene to be observed. In this paper we put forward a color night vision method based on the correlation between natural color image and dual band night image. Color display is attained through dual-band low light level images and their fusion image. Actual color image of the similar scene is needed to obtain color night vision image, the actual color image is decomposed to three gray-scale images of RGB color module, and the short wave LLL image, long wave LLL image and their fusion image are compared to them through gray-scale spatial correlation method, and the color space mapping scheme is confirmed by correlation. Gray-scale LLL images and their fusion image are adjusted through the variation of HSI color space coefficient, and the coefficient matrix is built. Color display coefficient matrix of LLL night vision system is obtained by multiplying the above coefficient matrix and RGB color space mapping matrix. Emulation experiments on general scene dual-band color night vision indicate that the color display effect is approving. This method was experimented on dual channel dual spectrum LLL color night vision experimental apparatus based on Texas Instruments digital video processing device DM642.

The real-time dual band image fusion system with improved gray modulating fusion algorithm

This paper analyzes the low-level-light image and ultraviolet images characteristics, and then the low-level-light and ultraviolet dual band false color preprocessing and fusion hardware system is put forward. To this system, real-time performance is an important factor. This system contains two parts. The first part is a FPGA+SDRAM architecture noise reducing system. The time domain average filter is applied to this part, because it meets the real-time requirement and can effectively decrease the low-light-level and ultraviolet images flicker noise. The second part is the fusion system. Its core is the most advanced video processor TMS320C6711. The processors EDMA can operate smartly to achieve the dual channel images capturing, calibrating and false color displaying without the core processors interference, while this trait is especially useful to the dual band image fusion system. In this part, for real-time performance consideration, the improved gray modulating fusion algorithm is used. The improvements aim at using the maximum potential of the core processors architecture. This paper gives the hardware data flow of the time domain average filter algorithm, the image registration algorithm and the improved gray modulating fusion algorithm in detail, and the systems schematic is also included in this paper. This system achieves the low-level-light and ultraviolet images noise reducing, and solves the worldwide problem, the image registration. And most important it is a real-time hardware processing system and can be easy to integrate and equip.