  Cosmic ray (CR) identification and replacement are critical components of
imaging and spectroscopic reduction pipelines involving solid-state detectors.
We present deepCR, a deep learning based framework for CR identification and
subsequent image inpainting based on the predicted CR mask. To demonstrate the
effectiveness of this framework, we train and evaluate models on Hubble Space
Telescope ACS/WFC images of sparse extragalactic fields, globular clusters, and
resolved galaxies. We demonstrate that at a false positive rate of 0.5%, deepCR
achieves close to 100% detection rates in both extragalactic and globular
cluster fields, and 91% in resolved galaxy fields, which is a significant
improvement over the current state-of-the-art method LACosmic. Compared to a
multicore CPU implementation of LACosmic, deepCR CR mask predictions run up to
6.5 times faster on CPU and 90 times faster on a single GPU. For image
inpainting, the mean squared errors of deepCR predictions are 20 times lower in
globular cluster fields, 5 times lower in resolved galaxy fields, and 2.5 times
lower in extragalactic fields, compared to the best performing non-neural
technique tested. We present our framework and the trained models as an
open-source Python project, with a simple-to-use API. To facilitate
reproducibility of the results we also provide a benchmarking codebase.
