In the infrared small target detection system, CFAR (Constant False Alarm Rate) is a commonly used technology, but in the traditional single frame detection method, detection rate is requested to be improved while the false alarm rate is increasing. This paper proposes a threshold attenuation CFAR detection method based on Gauss distribution. After the preprocessing of infrared images, we came into the designing of CFAR detector based on Gauss distribution. According to the previous frame target location and attenuation of local threshold, the detection rate of the target neighbourhood can be improved to obtain the current target location. The experimental results show that the proposed method can effectively control the threshold, and under the precondition that the background clutter was suppressed by the global low false alarm rate, it can improve the local detection rate and reduce the probability of target loss.
As one of widely applied nonlinear smoothing filtering methods, median filter is quite effective for removing salt-andpepper noise and impulsive noise while maintaining image edge information without blurring its boundaries, but its computation load is the maximal drawback while applied in real-time processing systems. In order to solve the issue, researchers have proposed many effective fast algorithms and published many papers. However most of the algorithms are based on sorting operations so as to make real-time implementation difficult. In this paper considering the large scale Boolean calculation function and convenient shift operation which are two of the advantages of FPGA(Field Programmable Gate Array), we proposed a novel median value finding algorithm without sorting, which can find the median value effectively and its performing time almost keeps changeless despite how large the filter radius is. Based on the algorithm, a real-time median filter has been realized. A lot of tests demonstrate the validity and correctness of proposed algorithm.
The task of small target detection is to extract the small targets from the background image including clutter, noise and jitter background, so it is difficult to deal with. In this paper, after analyzing infrared small targets, noise and clutter model, we use a small window median filter to estimate the infrared background. Then using background cancelling method, that is, subtracting the estimated background from the source image, the resident image can be obtained. Finally, an adaptive threshold is used to segment the residual image to obtain the potential targets. Considering the computational load, the two-dimensional filter is simplified into a one-dimensional filter. Experimental results show that the algorithm achieved good performance and satisfy the requirement of real-time processing of large size infrared image.
Electronic digital image stabilization technique plays important roles in video surveillance or object acquisition.
Researchers have presented many useful algorithms, which can be classified to three kinds: gray based methods,
transformation based methods and feature based methods. When scenario is simple or flat, feature based methods
sometimes have imperfect results. Transformation based methods usually accompany large computation cost and high
computation complexity. Here we presented an algorithm based on gray projection which divided the whole image into
four sub-regions: the upper one, the bottom one, the left one and the right one. For making the translation estimation
easier, a central region is also chosen. Then the gray projections of the five sub-regions were counted. From the five pairs
of gray projections five group offsets including rotation and translation were obtained via cross correlation between
current frame and reference frame gray projections. Then according to the above offsets, the required parameters can be
estimated. The expected translation parameters(x axis offset and y axis offset) can be estimated via the offsets from the
central region image pair, the rotation angle can be calculated from the left four groups offsets. Finally, Kalman filter was
adopted to compute the compensation. Test results show that the algorithm has good estimation performance with less
than one pixel translation error and 10 percent rotation error. Based on this kind of gray projection algorithm, a real-time
electronic digital image stabilization system has been designed and implemented. System tests demonstrate the system
performance reaches the expected aim.