  Context. High-contrast exoplanet imaging is a rapidly growing field as can be
seen through the significant resources invested. In fact, the detection and
characterization of exoplanets through direct imaging is featured at all major
ground-based observatories. Aims. We aim to improve the signal-to-noise ratio
(SNR) achievable for ground-based, adaptive-optics assisted exoplanet imaging
by applying sophisticated post-processing algorithms. In particular, we
investigate the benefits of including time domain information. Methods. We
introduce a new speckle-suppression technique in data post-processing based on
wavelet transformation. This technique explicitly considers the time domain in
a given data set (specifically the frequencies of speckle variations and their
time dependence) and allows us to filter-out speckle noise. We combine our
wavelet-based algorithm with state-of-the-art principal component analysis
(PCA) based PSF subtraction routines and apply it to archival data sets of
known directly imaged exoplanets. The data sets were obtained in the L filter
where the short integration times allow for a sufficiently high temporal
sampling of the speckle variations. Results. We demonstrate that improvements
in the peak SNR of up to forty to sixty percent can be achieved. We also show
that, when combined with wavelet-denoising, the PCA PSF model requires
systematically smaller numbers of components for the fit to achieve the highest
SNR. The improvement potential is, however, data set dependent or, more
specifically, closely linked to the field rotation available in a given data
set: larger amounts of rotation allow for a better suppression of the speckle
noise. Conclusions. We have demonstrated that by applying advanced data
post-processing techniques, the contrast performance in archival high-contrast
imaging data sets can be improved.
