【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.更多还原

【Abstract】 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 algorithm.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 multi-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 advantages of good adaptability and can effectively compensate both isolated and blocked blind pixels.And the compensated image achieves noticeable resolution and edge details.更多还原