  We investigate the impact of transient noise artifacts, or {\it glitches}, on
gravitational wave inference, and the efficacy of data cleaning procedures in
recovering unbiased source properties. Due to their time-frequency morphology,
broadband glitches demonstrate moderate to significant biasing of posterior
distributions away from true values. In contrast, narrowband glitches have
negligible biasing effects owing to distinct signal and glitch morphologies. We
inject simulated binary black hole signals into data containing three common
glitch types from past LIGO-Virgo observing runs, and reconstruct both signal
and glitch waveforms using {\tt BayesWave}, a wavelet-based Bayesian analysis.
We apply the standard LIGO-Virgo-KAGRA deglitching procedure to the detector
data - we subtract the glitch waveform estimated by the joint {\tt BayesWave}
inference before performing parameter estimation with detailed compact binary
waveform models. We find that this deglitching effectively mitigates bias from
broadband glitches, with posterior peaks aligning with true values post
deglitching. This provides a baseline validation of existing techniques, while
demonstrating waveform reconstruction improvements to the Bayesian algorithm
for robust astrophysical characterization in glitch-prone detector data.
