基于人类视觉的快速自动调焦法

黄德天; 刘雪超; 张红胜; 赵晶丽

doi:10.3788/YJYXS20142905.0768

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基于人类视觉的快速自动调焦法

成像技术与图像处理 | 更新时间：2020-08-12

- 基于人类视觉的快速自动调焦法
- Fast auto-focusing method based on human visual system
- 液晶与显示 2014年29卷第5期页码：768-776
- 作者机构：
  
  1. 中国科学院长春光学精密机械与物理研究所,吉林长春,中国,130033
  2. 中国科学院研究生院北京,100049
  3. 华侨大学工学院,福建泉州,362021
  4. 中国科学院光学系统先进制造技术重点实验室,吉林长春,130033
- 作者简介：
- 基金信息：
  
  华侨大学科研基金（No.13BS416）();国家863高技术研究发展计划（No.2008AA7034320B）();国家自然科学基金（No.61203242）();物
- DOI：10.3788/YJYXS20142905.0768
  中图分类号： TP391
- 收稿日期：2014-01-02，
  
  修回日期：2014-02-23，
  
  纸质出版日期：2014-10-05
- 稿件说明：
移动端阅览
黄德天, 刘雪超, 张红胜, 赵晶丽. 基于人类视觉的快速自动调焦法[J]. 液晶与显示, 2014,29(5): 768-776

HUANG De-tian, LIU Xue-chao, ZHANG Hong-sheng, ZHAO Jing-li. Fast auto-focusing method based on human visual system[J]. , 2014,29(5): 768-776
黄德天, 刘雪超, 张红胜, 赵晶丽. 基于人类视觉的快速自动调焦法[J]. 液晶与显示, 2014,29(5): 768-776 DOI： 10.3788/YJYXS20142905.0768.

HUANG De-tian, LIU Xue-chao, ZHANG Hong-sheng, ZHAO Jing-li. Fast auto-focusing method based on human visual system[J]. , 2014,29(5): 768-776 DOI： 10.3788/YJYXS20142905.0768.

摘要

为了提高自动调焦算法的性能，对调焦评价函数和调焦搜索算法进行了研究。在分析人类视觉系统（HVS）特性研究成果的基础上，提出了一种基于HVS加权的小波调焦评价函数。根据视觉多通道特性和视觉敏感度带通特性，对不同方向、不同空间频带的小波高频系数分别赋予不同的权值。为了克服爬山法搜索速度慢的缺点，提出了一种基于SOM神经网络的搜索算法，采用训练完成的SOM神经网络预测镜头的最佳聚焦位置。实验结果表明，提出的小波调焦评价函数得到了比传统小波调焦评价函数更优的调焦特性曲线；采用本文的搜索算法平均仅需要采集处理7.3幅图像就能找到最佳聚焦位置，与基于全搜索的爬山法相比，节省了大量的搜索时间。说明提出的调焦方法不但可以得到符合人眼视觉的调焦效果，还可以实现快速自动调焦。

Abstract

In order to improve the performance of the auto-focusing algorithm

focus measure function and focus search algorithm are studied. On the basis of analyzing the research results of the characte-ristics of the human visual system (HVS)

a weighted wavelet focus measure function is proposed based on HVS. According to the visual multi-channel characteristics and the visual sensitivity band-pass characteristics

the different weights of the high frequency coefficients are set in different orientations and different spatial frequencies. To overcome the disadvantage of slow search speed of the mountain climbing searching algorithm

an improved searching algorithm is presented based on self-organizing map (SOM) neutral network. The well-trained SOM neutral network is applied to predicting the best focused lens position. The experimental results demonstrate that the characteristic of the proposed wavelet focus measure curve is better than that of traditional wavelet focus measure curve. Only 7.3 images need to be collected and processed averagely in the process of finding the best focused lens position by improved searching algorithm. Compared with the full search method

the proposed approach can reduce the search time greatly. It is concluded that this auto-focusing method can not only obtain the focusing effects in accordance with the human visual system

but also achieve auto-focusing rapidly.

关键词

Keywords

references

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