Image focus measure based on slant transform and variance

Li-qiang GUO; Lian LIU

doi:10.37188/OPE.2020.0555

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Image focus measure based on slant transform and variance

Information Sciences | 更新时间：2021-08-09

- Image focus measure based on slant transform and variance
- Optics and Precision Engineering Vol. 29, Issue 7, Pages: 1731-1739(2021)
- 作者机构：
  
  1.淮阴师范学院计算机科学与技术学院，江苏淮安 223300
  2.淮阴师范学院物理与电子电气工程学院，江苏淮安 223300
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.2020.0555
  CLC：
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Li-qiang GUO, Lian LIU. Image focus measure based on slant transform and variance. [J]. Optics and Precision Engineering 29(7):1731-1739(2021)
DOI：

Li-qiang GUO, Lian LIU. Image focus measure based on slant transform and variance. [J]. Optics and Precision Engineering 29(7):1731-1739(2021) DOI： 10.37188/OPE.2020.0555.

摘要

针对相机和电子显微镜等成像设备在自动对焦过程中聚焦测度易受噪声干扰的问题，将图像处理中的斜变换与统计学中的方差相结合，提出一种具有噪声鲁棒性的聚焦测度。将图像进行分块处理，便于后续获取每个局部子图像的清晰度指标。计算每一个子图像的斜变换来得到频域系数。在变换域中，计算中频系数的绝对值并对它进行累加求和作为各子图像的清晰度指标。最后，计算各子图像清晰度的方差，将该方差作为整幅图像的聚焦测度值。通过提取局部子图像的中频信息并结合全局的方差求解的方法可以使聚焦测度具有较高的噪声鲁棒性；通过在LIVE图像数据库的实验结果表明：本文方法的噪声鲁棒性优于现有经典算法，其SDA和离散度客观评价指标相比于鲁棒性较好的索贝尔梯度熵算法平均提高了20.27%和125.61%。

Abstract

Focus measure is easy to be interfered by the noise in the autofocusing process for a camera and electronic microscope. In order to solve this problem， we combine the slant transform in image processing with the variance in statistics to propose a novel focusing measure with noise robustness. First， the image is divided into small blocks to facilitate the subsequent acquisition of the sharpness index of each local sub image. Then， we perform the slant transform on each sub image to obtain the frequency coefficients. In the transform domain， we calculate the absolute value of the mid-frequency coefficient， and perform the summation operation to obtain the sharpness index of each sub image. Finally， the variance of each sub image sharpness index is calculated， and the result is taken as the final focus measure of the whole image. By extracting the mid-frequency information of local sub images and solving the global variance， the proposed focus measure has strong noise robustness. The experiments on LIVE image database indicate that compared with the typical focus measure， the noise robustness of the proposed method is better than the existing classical algorithms， where the sharpness detection ability （SDA） and the discreteness evaluation indexes are improved by 20.27% and 125.61% averagely.

关键词

图像处理聚焦测度斜变换方差

Keywords

image processingfocus measureslant transformvariance

references

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School of Mathematics and Statistics， Changchun University of Science and Technology

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