Multi-resolution filtering: an empirical method for isolating faint, extended emission in Dragonfly data and other low resolution images
Pieter van Dokkum, Deborah Lokhorst, Shany Danieli, Jiaxuan Li,, Allison Merritt, Roberto Abraham, Colleen Gilhuly, Johnny P. Greco

TL;DR
This paper introduces Multi-resolution filtering (MRF), an empirical technique that isolates faint, extended emission in low-resolution images by leveraging high-resolution data, demonstrated on Dragonfly images and implemented as an open-source Python package.
Contribution
The paper presents a novel empirical method, MRF, for extracting faint, large-scale emission in low-resolution images using high-resolution data, with an open-source implementation.
Findings
Successfully isolated faint extended emission in various galaxy images.
Identified a new very faint galaxy in the M101 field.
Demonstrated the method's effectiveness across different datasets.
Abstract
We describe an empirical, self-contained method to isolate faint, large-scale emission in imaging data of low spatial resolution. Multi-resolution filtering (MRF) uses independent data of superior spatial resolution and point source depth to create a model for all compact and high surface brightness objects in the field. This model is convolved with an appropriate kernel and subtracted from the low resolution image. The halos of bright stars are removed in a separate step and artifacts are masked. The resulting image only contains extended emission fainter than a pre-defined surface brightness limit. The method was developed for the Dragonfly Telephoto Array, which produces images that have excellent low surface brightness sensitivity but poor spatial resolution. We demonstrate the MRF technique using Dragonfly images of a satellite of the spiral galaxy M101, the tidal debris…
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