Multi-frame blind deconvolution and phase diversity with statistical inclusion of uncorrected high-order modes

TL;DR

This paper introduces a statistical enhancement to multi-frame blind deconvolution (MFBD) that incorporates high-order atmospheric turbulence modes using Kolmogorov statistics, improving image restoration quality in ground-based telescopic imaging.

Contribution

The authors develop a modified MFBD model that includes statistical tails of high-order aberrations, enhancing restoration accuracy and robustness against errors in turbulence estimation.

Findings

SD improves image contrast and power spectra accuracy.

SD reduces wavefront fitting errors.

Results are robust to moderate errors in turbulence parameters.

Abstract

Images collected with ground-based telescopes suffer blurring and distortions from turbulence in Earth's atmosphere. Adaptive optics (AO) can only partially compensate for these effects. Neither multi-frame blind deconvolution (MFBD) nor speckle techniques restore AO-compensated images to the correct power spectrum and contrast. MFBD can only compensate for a finite number of low-order aberrations, leaving a tail of uncorrected high-order modes. Speckle restoration of AO-corrected data depends on calibrations of the AO corrections and assumptions regarding the height distribution of atmospheric turbulence. We seek to develop an improvement to MFBD that combines speckle's usage of turbulence statistics to account for high-order modes with the ability of MFBD to sense low-order modes that can be partially corrected by AO and/or include fixed or slowly changing instrumental aberrations. We modify the image-formation model, supplementing the fitted low-order wavefront aberrations with tails of random high-order aberrations. These tails follow Kolmogorov statistics scaled to estimated or measured values of Fried's parameter, r0, that characterize the strength of the seeing at the moment of data collection. We refer to this as statistical diversity (SD). We test MFBD with SD with noise-free synthetic data, simulating many different r0 and numbers of AO-corrected modes. SD improves the contrasts and power spectra of restored images, both in accuracy and in consistency with varying r0, without penalty in processing time. With focus diversity (FD), the results are almost perfect. SD also reduces errors in the fitted wavefront parameters. MFBD with SD and FD seems resistant to several percents of error in r0. Adding SD to MFBD shows great promise for improving contrasts and power spectra in restored images. Further studies with real data are merited.