离子发动机加速栅极腐蚀深度的DFF测量与数值模拟

使用聚焦深度表面测量(DFF)方法对加速栅极下游表面腐蚀深度进行了测量,并将测量结果与数值模拟结果进行了比较,所使用的数值方法为PIC-Monte Carlo方法.利用数值模拟程序对离子发动机栅极腐蚀进行了数值模拟.以氙为推进剂,栅极材料为钼.用蒙特卡罗方法模拟了氙离子与中性氙原子之间的电荷交换碰撞.模拟得到了加速栅极下游表面离子溅射腐蚀的深度分布,腐蚀模式与"Pits and grooves"模式相吻合.

Adaptive window iteration algorithm for enhancing 3D shape recovery from image focus

Depth from focus(DFF)is a technique for estimating the depth and three-dimensional(3D)shape of an object from a multi-focus image sequence.At present,focus evaluation algorithms based on DFF technology will always cause inaccuracies in deep map recovery from image focus.There are two main reasons behind this issue.The first is that the window size of the focus evaluation operator has been fixed.Therefore,for some pixels,enough neighbor information cannot be covered in a fixed window and is easily disturbed by noise,which results in distortion of the model.For other pixels,the fixed window is too large,which increases the computational burden.The second is the level of difficulty to get the full focus pixels,even though the focus evaluation calculation in the actual calculation process has been completed.In order to overcome these problems,an adaptive window iteration algorithm is proposed to enhance image focus for accurate depth estimation.This algorithm will automatically adjust the window size based on gray differences in a window that aims to solve the fixed window problem.Besides that,it will also iterate evaluation values to enhance the focus evaluation of each pixel.Comparative analysis of the evaluation indicators and model quality has shown the effectiveness of the proposed adaptive window iteration algorithm.