  Instrumental and environmental transient noise bursts in gravitational-wave
detectors, or glitches, may impair astrophysical observations by adversely
affecting the sky localization and the parameter estimation of
gravitational-wave signals. Denoising of detector data is especially relevant
during low-latency operations because electromagnetic follow-up of candidate
detections requires accurate, rapid sky localization and inference of
astrophysical sources. NNETFIX is a machine learning-based algorithm designed
to remove glitches detected in coincidence with transient gravitational-wave
signals. NNETFIX uses artificial neural networks to estimate the portion of the
data lost due to the presence of the glitch, which allows the recalculation of
the sky localization of the astrophysical signal. The sky localization of the
denoised data may be significantly more accurate than the sky localization
obtained from the original data or by removing the portion of the data impacted
by the glitch. We test NNETFIX in simulated scenarios of binary black hole
coalescence signals and discuss the potential for its use in future low-latency
LIGO-Virgo-KAGRA searches. In the majority of cases for signals with a high
signal-to-noise ratio, we find that the overlap of the sky maps obtained with
the denoised data and the original data is better than the overlap of the sky
maps obtained with the original data and the data with the glitch removed.
