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图像智能聚焦系统的设计基于改进的SMD功能和RBF算法

Qianwei Deng^1,2, Chee-Onn Wong², Roopesh Sitharan²

¹School of Film and Television Media, Wuchang University of Technology, Wuhan, Hubei, China.

|August 8, 2024

概括

本研究引入了使用数字图像处理和增强光学传感器数据融合的改进焦点模型. 新的总和模分差方法显著提高了实时感知精度和动态控制精度.

科学领域:

数字图像处理是数字图像处理.
光学传感器技术的技术.
数据融合方法的方法.

背景情况:

光学传感器中的传统焦点模型的准确性不足.
实时感知和动态控制的局限性阻碍了先进的应用.
在开放环境中整合计算和物理过程的挑战.

研究的目的:

为了克服传统焦点模型的精度限制.
为了提高实时感知和动态控制的精度.
在开放的计算物理环境中促进无互动和集成.

主要方法:

开发了基于值评估的增强总和模块差异 (SMD) 评估函数.
利用值细分来计算灰色值并确定最佳的图像细分值.
将已识别的值应用于使用辐射基函数 (RBF) 算法的焦点搜索策略.
在Zynq开发平台上实现智能聚焦系统.

主要成果:

改进的SMD评估功能快速识别灰色差异曲线的峰值点.
该方法准确地确定了最佳焦平面位置.
显示了对焦模型灵敏度的显著增强.

结论:

提议的增强的SMD功能和基于RBF的聚焦策略显著提高了聚焦精度和灵敏度.
在Zynq平台上的智能聚焦系统为实时感知提供了强大的解决方案.
这项研究有助于更精确,更完整的计算物理系统.

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