基于图像分割和配准的红外焦平面阵列非均匀校正算法

在目标成像跟踪和制导领域，存在大量由缓慢移动背景和相对背景运动的点目标构成的红外图像序列，传统的基于场景的红外焦平面非均匀校正算法在解决此类图像中存在困难。提出了基于图像分割和配准的红外焦平面非均匀校正算法(简称S—R算法)，通过将图像背景和运动点目标分离，利用图像配准的方法完成图像背景的非均匀校正和点目标位置处错误固定噪声参数的补偿，最终完成整个焦平面探测单元固定噪声参数的估计，从而有效解决了这类红外图像序列的非均匀校正问题。S—R算法具有噪声参数估计精度高、收敛速度快和计算复杂度低等优点。文中最后用仿真数据对上述结论进行验证。

Image Segmentation and Registration-Based Algorithm for Nonuniformity Correction of Infrared Focal Plane Arrays

There are a lot of infrared image sequences consisting of slowly moving background and point object with relative motion t in the field of target imaging, tracing and navigation, but the traditional scene-based algorithm for nonuniformity correction (NUC) of infrared focal planes arrays (IRFPA) had difficulty in solving these types of images. In the paper, a image segmentation and registration-based algorithm (S-R algorithm) had been proposed. Through separating the background and the point object, the former was corrected by the registration-based algorithm and the error fixed-pattern noise (FPN) parameters in the point object was compensated. So the FPN in the IRPFA was estimated completely and the NUC was solved efficiently. S-R algorithm has the advantage of accurate estimation of FPN, fast convergence rate and low computational complexity. At the end the conclusions were verified by the simulation data.