Multi-resolution filtering: an empirical method for isolating faint, extended emission in Dragonfly data and other low resolution images

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 surrounding M51, two ultra-diffuse galaxies in the Coma cluster, and the galaxy NGC5907. As part of the analysis we present a newly-identified very faint galaxy in the filtered Dragonfly image of the M101 field. We also discuss variations of the technique for cases when no low resolution data are available (self-MRF and cross-MRF). The method is implemented in mrf, an open-source MIT licensed Python package.