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

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.