Linnik point spread functions, time-reversed logarithmic diffusion equations, and blind deconvolution of electron microscope imagery

TL;DR

This paper introduces a non-iterative blind deconvolution method for sharpening electron microscope images using a Linnik point spread function and a logarithmic diffusion equation, effective under specific Fourier transform conditions.

Contribution

The paper adapts a previously successful deconvolution technique to nanoscale microscopy images, employing a Linnik PSF model and a backward Fourier space diffusion process for image sharpening.

Findings

Effective for SEM and HIM images with specific Fourier properties

Partial deconvolution yields better results than total deconvolution

Requires interactive parameter tuning and familiarity with microscopy images

Abstract

A non iterative direct blind deconvolution procedure, previously used successfully to sharpen Hubble Space Telescope imagery, is now found useful in sharpening nanoscale scanning electron microscope (SEM) and helium ion microscope (HIM) images. The method is restricted to images $g(x,y)$, whose Fourier transforms $\hat{g}(\xi,\eta)$ are such that $log~|\hat{g}(\xi,0)|$ is globally monotone decreasing and convex. The method is not applicable to defocus blurs. A point spread function in the form of a Linnik probability density function is postulated, with parameters obtained by least squares fitting the Fourier transform of the preconditioned microscopy image. Deconvolution is implemented in slow motion by marching backward in time, in Fourier space, from $t = 1$ to $t = 0$, in an associated logarithmic diffusion equation. Best results are usually found in a partial deconvolution at time $\bar{t}$, with $0 < \bar{t} < 1$, rather than in total deconvolution at $t=0$. The method requires familarity with microscopy images, as well as interactive search for optimal parameters.