fiDrizzle-MU: A Fast Iterative Drizzle with Multiplicative Updates

TL;DR

fiDrizzleMU is a novel iterative multiplicative update algorithm for image co-addition that improves anti-aliasing and noise reduction, enabling better recovery of faint structures in astronomical images.

Contribution

It introduces a new multiplicative update framework for image stacking, replacing traditional additive correction methods, and demonstrates its effectiveness on JWST data.

Findings

Enhanced anti-aliasing and noise reduction in stacked images

Successful reconstruction of a gravitational lensing candidate

Improved recovery of faint, extended astronomical structures

Abstract

We propose fiDrizzleMU, an algorithm for co-adding exposures via iterative multiplicative updates, replacing the additive correction framework. This method achieves superior anti-aliasing and noise reduction in stacked images. When applied to James Webb Space Telescope data, the fiDrizzleMU algorithm reconstructs a gravitational lensing candidate that was significantly blurred by the pipeline's resampling process. This enables the accurate recovery of faint and extended structures in high-resolution astronomical imaging.