How to coadd images: II. Anti-aliasing and PSF deconvolution

TL;DR

This paper introduces a new method for co-adding under-sampled images that combines anti-aliasing and PSF deconvolution, improving image restoration and artifact reduction at the cost of increased computation.

Contribution

A novel co-adding technique integrating iterative reweighted least squares with divide-and-conquer algorithms for enhanced image restoration.

Findings

Outperforms previous methods in restoring extended source profiles

Reduces ringing artifacts in the combined images

Provides higher PSNR in simulated observations

Abstract

We have developed a novel method for co-adding multiple under-sampled images that combines the iteratively reweighted least squares and divide-and-conquer algorithms. Our approach not only allows for the anti-aliasing of the images but also enables PSF deconvolution, resulting in enhanced restoration of extended sources, the highest PSNR, and reduced ringing artefacts. To test our method, we conducted numerical simulations that replicated observation runs of the CSST/VST telescope and compared our results to those obtained using previous algorithms. The simulation showed that our method outperforms previous approaches in several ways, such as restoring the profile of extended sources and minimizing ringing artefacts. Additionally, because our method relies on the inherent advantages of least squares fitting, it is more versatile and does not depend on the local uniformity hypothesis for the PSF. However, the new method consumes much more computation than the other approaches.