Study of an image autofocus method based on power threshold function wavelet reconstruction and a quality evaluation algorithm

作     者：Yan, Zhidan Chen, Guo Xu, Wenyi Yang, Chunmei Lu, Yang 

作者机构：China Univ Petr East China Coll Informat & Control Engn Changjiangxi Rd 66 Qingdao 266580 Shandong Peoples R China Tianjin Univ State Key Lab Precis Measuring Technol & Instrume Univ Weijin Rd 92 Tianjin 300072 Peoples R China 

出 版 物：《APPLIED OPTICS》 (Appl. Opt.)

年 卷 期：2018年第57卷第33期

页      面：9714-9721页

核心收录：

学科分类：070207[理学-光学] 07[理学] 08[工学] 0803[工学-光学工程] 0702[理学-物理学] 

基　　金：State Key Laboratory of Precision Measurement Technology and Instruments [PIL1604] Fundamental Research Funds for the Central Universities [14CX02204A, 18CX02108A] 

主　　题：Image processing Image processing algorithms Image quality Image resolution Imaging systems Signal processing 

摘      要：As a key component in optical microscopy imaging systems, autofocus technology has a significant effect on imaging quality. In this paper, an optical microscopy autofocus method that includes a wavelet denoising algorithm based on a power threshold function and a Brenner image quality evaluation algorithm is presented. Experimental results show that the power threshold function wavelet denoising algorithm, which can be adopted to obtain more realistic optical images, is superior to the traditional soft, hard, hyperbolic, and exponential threshold functions in terms of peak signal-to-noise ratio, signal-to-noise ratio, mean squared error, and histogram indicators;moreover, compared to the Roberts, sum modulus difference (SMD), and energy gradient functions, the Brenner image quality evaluation algorithm can be used to quickly and accurately lock onto the focal plane. By integrating and applying these two core algorithms in the autofocus image acquisition system of a microscope, the image sharpness and focusing quality are greatly improved, which benefits the further evaluation of images. (C) 2018 Optical Society of America