  Gravitational wave astronomy has become a reality after the historical
detections accomplished during the first observing run of the two advanced LIGO
detectors. In the following years, the number of detections is expected to
increase significantly with the full commissioning of the advanced LIGO,
advanced Virgo and KAGRA detectors. The development of sophisticated data
analysis techniques to improve the opportunities of detection for low
signal-to-noise-ratio events is hence a most crucial effort. We present in this
paper one such technique, dictionary-learning algorithms, which have been
extensively developed in the last few years and successfully applied mostly in
the context of image processing. However, to the best of our knowledge, such
algorithms have not yet been employed to denoise gravitational wave signals. By
building dictionaries from numerical relativity templates of both, binary black
holes mergers and bursts of rotational core collapse, we show how
machine-learning algorithms based on dictionaries can be also successfully
applied for gravitational wave denoising. We use a subset of signals from both
catalogs, embedded in non-white Gaussian noise, to assess our techniques with a
large sample of tests and to find the best model parameters. The application of
our method to the actual signal GW150914 shows promising results.
Dictionary-learning algorithms could be a complementary addition to the
gravitational wave data analysis toolkit. They may be used to extract signals
from noise and to infer physical parameters if the data are in good enough
agreement with the morphology of the dictionary atoms.
