Focus Evaluation Method with Independent of Image Content

XIA Xiaohua; CHAI Yulin; YUE Pengju; YANG Zhi; QIN Xufang

doi:10.7652/xjtuxb202501011

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Focus Evaluation Method with Independent of Image Content

更新时间：2026-01-06

- Focus Evaluation Method with Independent of Image Content
- Journal of Xi’an Jiaotong University Vol. 59, Issue 1, Pages: 116-124(2025)
- 作者机构：
  
  长安大学道路施工技术与装备教育部重点实验室， 710000，西安
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202501011
  CLC： TP399
- Received：12 May 2024，
  
  Online First：10 September 2024，
  
  Published：10 January 2025
- 稿件说明：
移动端阅览
XIA Xiaohua, CHAI Yulin, YUE Pengju, et al. Focus Evaluation Method with Independent of Image Content[J]. Journal of Xi’an Jiaotong University, 2025, 59(1): 116-124.
DOI：

XIA Xiaohua, CHAI Yulin, YUE Pengju, et al. Focus Evaluation Method with Independent of Image Content[J]. Journal of Xi’an Jiaotong University, 2025, 59(1): 116-124. DOI： 10.7652/xjtuxb202501011.

摘要

针对现有的聚焦程度评价方法在图像场景内容改变时不能正确评价图像聚焦程度的问题，基于“分类+拟合”的思想，提出了一种与高斯模糊标准差完全等价的图像聚焦程度评价方法。首先，建立了以有限高斯模糊标准差为标记的图像聚焦程度分类数据集；然后，构建了用于提取图像高斯模糊标准差分类分数的非对称核卷积神经网络（AKC-net）；最后，采用三次样条插值函数拟合AKC-net全连接层输出的分类分数以及对应的高斯模糊标准差，以最大分数对应的标准差作为图像的聚焦程度评价结果，并在Waterloo数据集和实际拍摄图像上分别进行仿真实验和实拍实验。结果表明：所提方法在不同聚焦图像上分类的平均准确率可达到97.7%，得到的评价结果与高斯模糊标准差真值的均方根误差和平均绝对误差均小于0.07，且实际拍摄图像的聚焦测度值与图像内容无关，实现了图像聚焦程度的绝对评价。

Abstract

In response to the challenge where current focus evaluation methods struggle to accurately assess image focus amidst fluctuations in image scene content

a focus evaluation method

fully equivalent to Gaussian blur standard deviation

is introduced based on the “classification + fitting” concept. Firstly

a dataset for image focus classification

annotated with finite Gaussian blur standard deviations

is established. Subsequently

an asymmetric kernel convolution neural network (AKC-net) is developed to derive image Gaussian blur standard deviation classification scores. Finally

a cubic spline interpolation function is applied to fit the classification score of the AKC-net fully connected layer output and the corresponding Gaussian blur standard deviation

using the standard deviation linked to the maximum score as the focus evaluation for the images. Simulation experiments and real shooting experiment are carried out on the Waterloo dataset and real shooting images respectively. The experimental findings indicate that the proposed method achieves an average classification accuracy of up to 97.7% across various focus images. The root mean square error and mean absolute error between the obtained evaluation results and the true Gaussian blur standard deviation values are less than 0.07. Furthermore

the focus measurement values derived from real shooting images are independent of the image content

enabling the absolute evaluation of the image focus.

关键词

Keywords

references

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