基于脑模型控制器的红外背景预测算法

在分析云背景红外图像空间分布上存在混沌现象的基础上,提出一种基于脑模型控制器的红外背景预测算法.该方法利用混沌具有短时可预测性的特点,对云背景图像进行预测,并根据云背景杂波和运动目标的混沌特性差异修正预测模型.仿真结果表明,该算法能有效地提高云背景的预测准确度,预测残差符合白噪音特性,对云背景杂波具有良好的抑制效果,能显著提高目标的信杂比,从而改善目标的检测性能.

Infrared Background Prediction Algorithm Based on Cerebellar Model Articulation Controller

 In order to predict the spectral reflectance of recto-verso halftone images,the scattering of light within paper and the ink penetration in the substrate is considered and the color spectral reflectance precise prediction model for recto-verso halftone images is proposed.Based on the assumption that the colorant is non-scattering,the paper is strong scattering substrate and both the colorant and ink have the same reflective index,the light along oblique path from the Williams-Clapper model is introduced for taking into account ink spreading.By the multiple internal reflections between the paper substrate and the print-air interface of light,a phenomenon occurs when printing an ink halftone in superposition with one or several solid inks.The ink-spreading model includes nominal-to-effective dot area coverage functions for each of the different ink overprint conditions by the least square curve fitting method and the network structure of multiple reflections.The modeled and the measured colors agree very well,which confirms the validity of the used model,and provides a theoretical foundation for color prediction analysis of recto-verso halftone images and the development of prints quality detection system.