This paper presents an effective fixed pattern noise (FPN) removal method (PCA wavelet method). Compared with the traditional filtering method and statistical method, PCA wavelet method has better denoising effect. Compared with the optimization method, PCA wavelet method has faster processing speed. It is a comprehensive FPN removal method. Compared with the optimization method, this method does not need complicated parameter adjustment process, but has similar denoising effect with the optimization method. This method is very suitable for the engineering application which has certain requirements for calculation speed and denoising effect.
This paper first studies motion-related data driven tracking methods and analyzes their insufficiency and applications, then introduces the idea of Mean Shift and constructs kernel probability density based target model in accordance with statistical characteristics of infrared dim and small target in terms of statistical characteristics based difference between the infrared dim and small target and noise, and finally explores key problems such as target template extracting from tracking infrared dim and small target, tracking location determination and target model updating. The experiment results have shown that this motion-related based tracking method incorporating target gray scale statistical characteristics achieves effective combination of two tracking patterns by integrating advantages of both and thus significantly improves accuracy of tracking infrared dim and small target.
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