An evaluation criterion of infrared image complexity based on background optimal filter scale

Abstract

An evaluation of infrared image complexity is proposed based on the background optimal filtering to solve the problem that the traditional methods have given poor results in the background evaluation. Meanwhile, the optimal filtering scale for infrared image filtering can be given by this method, it will provide a guidance for optimal infrared image filtering. First, we generate the Gaussian simulated target and fuse it to the infrared image to obtain the real infrared image with the simulated target. Then, this image is filtered in different scales and the signal-to-noise ratio of the target after filtering is calculated. Finally, the maximal value of signal-to-noise ratio of the target is used as the background optimal filter scale, to evaluate the infrared image complexity. Besides, the infrared filtering scale is deduced by establishing the mathematic model, and then the mathematical expression of optimal filtering scale is obtained. A lot of experiments indicate that: 1) The mathematical expression of optimal filtering scale agrees with the experimental results. 2) The result of our method is better than that of the traditional methods based on information entropy. Because the optimal filtering scale is obtained by using our method, we can use this scale to filter the infrared image to effectively detect a small target. 3) When the scale of simulated target increases, the optimal filtering scale increases accordingly. So, when we calculate the infrared image complexity, the scale of simulated target must be the same. We can compare the infrared image complexity between different images. Moreover, the optimal filtering scale is independent of the intensity of simulated target. 4) The effect of Gaussian and Butterworth high-pass filter is better than that of the ideal high-pass filter in the proposed method. 5) The infrared image complexity can be analyzed by the proposed method effectively. Moreover, changes of different image contents can be analyzed by using the optimal filtering scale.