基于场景的空间投影估计和时域迭代非均匀性校正算法

本文提出了一种基于场景的空间投影估计和时域迭代非均匀性校正算法。本算法采用空间投影估计来估算图像的全局平移，并且在相邻帧之间进行时域迭代。具体分为下述三个主要步骤：首先，我们提出了一种基于配准准则的投影估计。然后，我们采用相邻帧的迭代校正来保证较快的收敛速度以及较少的非均匀性残留，同时保证基本没有鬼影。最终，我们将本算法移植到基于FPGA的硬件系统。通过评价标准测试了本算法的性能，并展示了在单调运动下系统非均匀性校正的实际效果。一段添加了模拟非均匀性的干净视频被用来对比此算法和阈值自适应最小均方差算法以及全偏差算法。在本文结尾，我们得到了结论，并对后期的工作进行了展望。

Space projection estimator and temporal iteration scene-based non-uniformity correction algorithm

In this paper, an improved scene-based non-uniformity correction (SBNUC) algorithm called space projection estimator and temporal iteration (SPETI) is proposed. This method estimates the global translation by the projection estimator and iterates between several adjacent frames. The detailed method includes three main steps: First, we develop a new projection estimator for the registration with a criterion. Then, correlation of adjacent frames, together with iteration strategy between them are used in order to get fast and reliable fixed-pattern noise (FPN) reduction with low few ghosting artifacts. Finally, this algorithm is immigrated into a FPGA-based hardware system. We test the performance of our algorithm by the evaluation indexes, and demonstrate the actual effect of correcting the non-uniformity under a monotonous motion on the system. In order to compare with the gated adaptive least mean square (GALMS) method and the total variation (TV) method, a clean infrared image sequences with synthetic non-uniformity is studied. Normal distributed gain and offset non-uniformity are applied to the image sequences to study the relationship of iteration times and level of non-uniformity. At the end of this paper, we make a conclusion and perspective of our research work.