3D Image Restoration Using Multiple Space-Varying Point Spread Functions

Abstract

Abstract:Model-based image restoration methods using multiple space-varying point spread functions (PSFs) enable microscopy of optically thick samples. Efficient PSF representation using an orthonormal basis reduces computational complexity and renders the methods practical for use. 2017 Optical Society of America IntroductionThree dimensional (3D) fluorescent images of optically thick samples [>5 µm thick with a space variant (SV) refractive index (RI) distribution within the imaging volume contributing to an optical path length change]) exhibit SV spherical aberration, due to the RI mismatch between the imaging layers and the design parameters of the system [1]. We have been developing restoration methods that address this aberration using different mathematical approximations of the SV imaging model [1][2][3]. A recent extension of the point-spread function (PSF) model to predict more accurately sample-induced aberration [4] and the use of an orthonormal basis [2] for the efficient representation of the resulting SV PSFs have enable the development of a new restoration method that accounts for space-variant imaging [3], thereby extending our earlier methods that account only for depth-variant imaging [1,2]. Tradeoffs between accuracy and complexity as well as the needs of an imaging application guide the restoration method choice.