Research Of Detection Of Infrared Small Target In The Complex Cloud Background

The detection problem of small targets of IR images in the complex cloud background is a hot spot in infrared imaging system nowadays. Exploring and studying the new small target detection theory and algorithms, as well as how to test theories for the existing situation is very important, with far-reaching significance. Firstly, some relevant concepts of small target and mathematical description of IR image are presented. Then the three elements (target, background and noise) of small target detection in the IR image are analyzed. Small target can be not the highest in the gray-scale, but compared with the local context it is more prominent. That is to say, it should have certain contrast in the local background and no shape information. Secondly, several commonly used background elimination algorithms and target detection algorithms are presented, and the differences were analyzed and compared. By analyzing various common spatial filtering methods, we can divide them into two categories: the first category is that we can adopt some effective spatial domain low-pass filter methods to estimate complex infrared background, then realize background cancellation in order to improve the SNR. The second category is effective through the introduction of some characteristics that realize the direct description of the target that has 1 high-frequency characteristics in the image. But, the algorithm proposed in this paper is based on the principles that the target shows the feature of high gray value and strong changes on the edge, and it surrounded by the ring that has low gray. It finds out the surrounding connectivity ring of small target though morphological gradient image, then determined the position of small target. Simulation results show that the algorithm is correct and effective, and it is very good at filtering background and noise, reducing the false alarm rate.