The Detection Of Targets In Infrared Image

Based on the different characteristics of background, small targets and noise in infrared images, the research is conducted in two different ways in this paper. One is based on the characteristic of small targets. First, one image is filtered by a high pass filter and the image mainly including the small targets and noise are obtained. Then, using the algorithms with wavelet transformation presented in this paper, which are based on the soft-threshold denoising and the relevant denoising, the small target can be detected. At the same time, the simulation result shows that the performance of the algorithm that is based on the soft-threshold denoising is better than the algorithm that is based on the relevant denoising. The other is mainly depended on the background rejection to detect the small target. There are two different situations in this field. One aims at the image sequences. Using the LMedS (Least median of squares) algorithm and the temporal median filter algorithm independently, the background rejection can be realized, and the changed parts in the image sequences that are mainly caused by the small target are obtained. The other aims at one image. Using the mathematic morphology algorithm, the background rejection can be realized and the image mainly including the small target can be obtained. Then, the images that are obtained by the LMedS algorithm, the temporal median filter algorithm and the mathematic morphology algorithm are processed and the small target is detected. At the same time, the simulation result shows that the performance of the mathematic morphology algorithm is the best, the performance of the temporal median filter algorithm ranks second, and the performance of the LMedS algorithm is the worst.