Multiframe deconvolution with space-variant point-spread functions by use of inverse filtering and fast Fourier transform

Abstract

A procedure for deconvolution of multiple images of the same object with space-variant point-spread functions (PSFs) is presented. It is based on expressing deconvolution with inverse filtering as convolution with kernels corresponding to inverse PSFs. Sets of basis functions are made from these inverse PSFs, given at discrete sample points, through Karhunen-Loève (K-L) decomposition. The entire field of view can then be convolved with the K-L kernels. Coadding the results using continuous maps of expansion weights, interpolated for every pixel between the sample points, results in an image that is deconvolved with smoothly varying PSFs that match the discrete measurements. A demonstration data set is used to show how the transition between the grid points improves deconvolutions compared to piecewise deconvolution and mosaicking by avoiding the blending of discontinuities at the interfaces between adjacent subfields.