Deconvolved Image Restoration from Autocorrelations

TL;DR

This paper introduces an iterative I-divergence optimization method to simultaneously invert auto-correlations and deconvolve images, enabling high-resolution reconstruction from blurred measurements in imaging applications.

Contribution

It presents a novel combined approach for auto-correlation inversion and deconvolution using I-divergence optimization, inspired by Bayesian methods.

Findings

Successfully recovered signals from blurred auto-correlations.

Analyzed cases of blurred objects and band-limited Fourier measurements.

Demonstrated improved image reconstruction quality.

Abstract

Recovering a signal from auto-correlations or, equivalently, retrieving the phase linked to a given Fourier modulus, is a wide-spread problem in imaging. This problem has been tackled in a number of experimental situations, from optical microscopy to adaptive astronomy, making use of assumptions based on constraints and prior information about the recovered object. In a similar fashion, deconvolution is another common problem in imaging, in particular within the optical community, allowing high-resolution reconstruction of blurred images. Here we address the mixed problem of performing the auto-correlation inversion while, at the same time, deconvolving its current estimation. To this end, we propose an I-divergence optimization, driving our formalism into a widely used iterative scheme, inspired by Bayesian-based approaches. We demonstrate the method recovering the signal from blurred auto-correlations, further analysing the cases of blurred objects and band-limited Fourier measurements.