Micro-image definition evaluation using multi-scale decomposition and gradient absolute value

Cui Guangmang; Zhang Keqi; Mao Lei; Xu Zhihai; Feng Huajun

doi:10.12086/oee.2019.180531

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Cui Guangmang, Zhang Keqi, Mao Lei, et al. Micro-image definition evaluation using multi-scale decomposition and gradient absolute value[J]. Opto-Electronic Engineering, 2019, 46(6): 180531. doi: 10.12086/oee.2019.180531

Citation:

Cui Guangmang, Zhang Keqi, Mao Lei, et al. Micro-image definition evaluation using multi-scale decomposition and gradient absolute value[J]. Opto-Electronic Engineering, 2019, 46(6): 180531. doi: 10.12086/oee.2019.180531

Micro-image definition evaluation using multi-scale decomposition and gradient absolute value

1.
Ningbo Yongxin Optics Co., Ltd., Ningbo, Zhejiang 315040, China
2.
State Key Lab of Modern Optical Instrumentation, Zhejiang University, Hangzhou, Zhejiang 310027, China
3.
School of Electronics and Information, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China

Fund Project: Supported by National Natural Science Foundation of China (61805063) and Zhejiang Province Postdoctoral Research

More Information

Corresponding author: Cui Guangmang, E-mail: cuigm@hdu.edu.cn

Received Date 16 October 2018

Revised Date 03 December 2018

Published Date 25 June 2019

Abstract

Abstract

Aimed at the problem of automatic focus and image system quality evaluation in microscopy imaging, a micro-image definition evaluation method is presented by combining multi-scale decomposition tools and absolute gradient operators. The multiscale and multidirectional non-subsampled Shearlet transform is utilized to decompose the input micro image into a low frequency sub-band image and a number of high frequency sub-band images. Combined with the anti-noise threshold setting, the gradient absolute sum values of each sub-band image were calculated. By using the different effects of image sharpness on the low-frequency and high-frequency sub-band coefficients, the ratio of the high-frequency to low-frequency gradient absolute value operator was taken as the final evaluation value of the microscopic image sharpness. The simulation experiment and actual experiments were carried out and the experimental results illustrated that the proposed approach has good monotonicity and anti-noise characteristics. Compared with other classic evaluation algorithms, the presented method obtained superior performance on sensitivity, stability and robustness. It has very good practical application values.
- micro-images /
- image definition evaluation /
- non-subsampled Shearlet transform (NSST) /
- gradient absolute value

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References

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Overview

Overview

Overview: As an important instrument for observing the micro world, optical microscope has been widely used in medical health, biological detection, industrial production and other related fields. The evaluation and determination of micro-image clarity has a direct impact on the accuracy of microscopy autofocus and has become an important index to measure the imaging quality of microscopy system. With the development of multimedia technology and digital image, the requirements for automation of microscopic instrument and equipment have been gradually improved, and more and more attention has been paid to the image process-based image sharpness evaluation algorithm, which is of great significance for realizing rapid and accurate microscopic autofocus and imaging system performance evaluation.

In this paper, a micro-image definition evaluation method is presented by combining multi-scale decomposition tools and absolute gradient operators. The non-subsampled Shearlet transform (NSST) is utilized to decompose the input micro image into a low frequency sub-band image and a number of high frequency sub-band images. NSST is a very effective image multi-scale decomposition tool proposed in recent years. Its mathematical structure is simple and has the characteristics of parabola scale, stronger directional sensitivity and optimal sparse. It can better express the image contour, edge, texture and other detail, which is suitable for image feature extraction and can provide more judgment information for the sharpness evaluation algorithm. Meanwhile, combined with the anti-noise threshold setting, the sum of gradient absolute (SAG) values of each sub-band image was calculated. The SAG operator replaces the square operator in the energy gradient function with absolute value calculation, which reduces the complexity of the calculation and improves the operation efficiency while representing the edge clarity of the image. At last, by using the different effects of image sharpness on the low-frequency and high-frequency sub-band coefficients, the ratio of the high-frequency to low-frequency gradient absolute value operator was taken as the final evaluation value of the microscopic image sharpness. In order to verify the performance of the algorithm, the simulation experiment and actual experiments were carried out. Image sequences with different degrees of blur were simulated and captured and several compared image sharpness evaluation methods were used to give out objective evaluation index values for these image sequences. The experimental results illustrated that the proposed approach has good monotonicity and anti-noise characteristics. Compared with other classic evaluation algorithms, the presented method obtained superior performance on sensitivity, stability and robustness. It has very good practical application values.