论文部分内容阅读
长波红外焦平面探测器以太空为背景成像后,图像灰度信息的统计量易受探测器固定坏元和随机坏元的影响,为解决此问题提出了一种自适应统计范围调整的红外图像灰度信息统计算法。其基本思想是在统计灰度信息时自适应设置一定的统计范围,尽可能排除坏元对统计灰度信息的影响。算法中建立线性模型描述当前帧统计得到的灰度信息与下一帧统计范围之间的关系,以统计均值的均方误差最小为准则调整线性模型参数。仿真实验表明,该算法能够追踪红外图像的背景渐变并自适应调整下一帧统计范围,克服了长波红外探测器中坏元对于统计过程的影响,为后续的处理提供准确的灰度信息。此外,该算法以一种序贯递推的方式执行,适合在对实时性要求较高的系统中实现。
After the imaging of long-wave infrared focal plane detector on the background of space, the statistics of image gray information are easily influenced by detector fixed bad element and random bad element. In order to solve this problem, an adaptive statistical range-adjusted infrared image Gray Information Statistics Algorithm. The basic idea is to set a certain range of statistics adaptively when calculating gray information, and to exclude as far as possible the influence of bad elements on statistical gray information. In the algorithm, a linear model is established to describe the relationship between the gray information obtained from the current frame statistics and the statistical range of the next frame. The linear model parameters are adjusted according to the minimum mean square error of the statistical mean. Simulation results show that the algorithm can track the background gradient of the infrared image and adaptively adjust the statistical range of the next frame, overcomes the influence of the bad element in the long-wave infrared detector on the statistical process, and provides accurate gray information for subsequent processing. In addition, the algorithm is implemented in a sequential recursive manner and is suitable for implementation in systems requiring high real-time performance.