基于混合自适应回归的红外盲元补偿算法  

A New Infrared Blind-Pixel Compensation Algorithm Based on Hybrid Autoregressive Modeling

作　　者：陈苏婷[1] 孟浩[1] 杨世洪[2] 陈金立[1] 

机构地区：[1]南京信息工程大学江苏省气象探测与信息处理重点实验室,南京210044 [2]中国科学院光电技术研究所三室,成都610209

出　　处：《计算机辅助设计与图形学学报》2015年第4期651-656,共6页Journal of Computer-Aided Design & Computer Graphics

基　　金：国家自然科学基金(61302188);中国博士后特别资助基金(2012T50510);江苏省高校自然科学研究重大项目(12KJA510001)

摘　　要：针对红外成像非均匀性产生的盲元与盲元簇问题,提出一种混合自适应回归的红外盲元补偿算法(HAM).首先对红外图像进行多尺度分解,并对各分解尺度构造非参数回归补偿;然后对各尺度间构建自回归参数模型实现从低分辨到高分辨的学习,进一步提高补偿精确度.HAM算法打破了现有补偿算法基于滤波和插值的传统思路,建立了基于多尺度分析的混合自适应回归补偿的新方法.实验结果表明,相比于传统的红外盲元补偿算法,HAM算法具有很好的适应性,对于具有大量孤立和盲元簇图像均能取得很好效果,且补偿后图像具有较好的清晰度与边缘细节.An infrared blind-pixel compensation algorithm is proposed based on hybrid autoregressive modeling (HAM). Combined with respective advantages of two modeling, a hybrid modeling algorithm is presented from the perspective of multi-scale. The infrared image is decomposed into multi-scaled sub-images by HAM algo-rithm. And then the blind-pixel is restored through the nonparametric regression estimation model in intra-scale; meanwhile the image is upsampled by the image parameter model in inter-scale. HAM establishes a set of mul-ti-scale blind-pixel compensation method based on hybrid parametric and nonparametric regression model. The experimental results show that, compared with the classical compensation algorithms, the algorithm has the ad-vantages of good adaptability and can effectively compensate both isolated and blocked blind pixels. And the compensated image achieves noticeable resolution and edge details.

关 键 词：盲元补偿 参数模型 非参数模型 自适应回归 

分 类 号：TN219[电子电信—物理电子学]