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.
@article{arxiv.2511.09881,
title = {fiDrizzle-MU: A Fast Iterative Drizzle with Multiplicative Updates},
author = {Shen Zhang and Lei Wang and Huanyuan Shan and Ran Li and Xiaoyue Cao and Yunhao Gao},
journal= {arXiv preprint arXiv:2511.09881},
year = {2025}
}