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More and more infrared imaging systems were applied in military and civil fields and the performance of detecting dim target is one important index for infrared imaging system. The performance of the method based on background suppression is declined with many false alarms and even loss target in image with clutter background. In order to improve the performance of dim target detection, the method base on target enhance is developed. In this paper, after analyzing the grey value distribution of target, background and noise in local area, the pixel's shape parameter was defined, maximum strategy was used to obtain one size of pixel's shape parameter, and enumerate strategy was used to search the possible target size. And then, one character filter based on shape parameter was designed to enhanced target and suppress clutter background. Compared with the method based on background suppression, experimental results show that proposal method is effective to enhance target and suppress clutter background.

Concerning the target losing problem caused by partial occlusion, uneven illumination and so on during the image tracking procedure by traditional algorithms, a new tracking method based on robust similarity measurement criteria is proposed in this paper. The measurement is carried out with the original gray image pair with no preprocessing such as feature extraction. First, a two-dimensional joint distribution histogram is set up to describe the gray value change between the pixel in the reference and its corresponding pixel in the real image. Then, the law of data distribution under uneven illumination and partial occlusion is concluded. At last, a strategy based on Hough transformation is adopted to calculate the measurement value on the joint histogram. The method is robust for the uneven illumination. And small partial occlusion has little influence on the output value of the similarity measurement. Comparing experimental results show the new method's efficiency.

Active learning is typically limited by the small sample problem which makes the resulting classifiers perform poorly, especially in the initial stages. To overcome this problem, in this paper, a novel framework - graph-incorporated active learning - is proposed, in which the selection pool is regarded as a graph. Its graph structure is applied to both improve sample selection criterion and provide the learner enough pseudo-labeled samples. By comparing with the state-of-theart technique, the experiments on benchmark datasets show that the improvement of the proposed method is significant, i.e., it can solve the small problem well. The framework is combined with, but is not limited to, SVM.

The performance of infrared focal plane array (IRFPA) is known to be affected by the presence of spatial fixed pattern noise (FPN) that is superimposed on the true image. Scene-based nonuniformity correction (NUC) algorithms are widely concerned since they only need the readout infrared data captured by the imaging system during its normal operation. A novel adaptive NUC algorithm is proposed using the sparse prior that when derivative filters are applied to infrared images, the filter outputs tends to be sparse. A change detection module based on results of derivative filters is introduced to avoid stationary object being learned into the background, so the ghosting artifact is eliminated effectively. The performance of the new algorithm is evaluated with both real and simulated imagery.