Superposition Imaging for Three-Dimensionally Space-Invariant Point Spread Functions

Abstract

In this paper, we propose an aberration compensation method using superposition imaging and inexpensive postprocessing. In the method, the focusing distance and optical axis position of an imaging system with aberrations are varied over certain ranges, and the resulting images are superposed to equalize the point spread function (PSF) within a three-dimensional region and remove space variance. A sharp image of an object with a large depth-of-field and field-of-view is then reconstructed by deconvolution of the superposed image using the effective three-dimensionally space-invariant PSF. The effectiveness of the proposed method was verified by simulations assuming defocus, the five Seidel aberrations, and vignetting.