  Radio frequency interference (RFI) have been an enduring concern in radio
astronomy, particularly for the observations of pulsars which require high
timing precision and data sensitivity. In most works of the literature, RFI
mitigation has been formulated as a detection task that consists of localizing
possible RFI in dynamic spectra. This strategy inevitably leads to a potential
loss of information since parts of the signal identified as possibly
RFI-corrupted are generally not considered in the subsequent data processing
pipeline. Conversely, this work proposes to tackle RFI mitigation as a joint
detection and restoration that allows parts of the dynamic spectrum affected by
RFI to be not only identified but also recovered. The proposed supervised
method relies on a deep convolutional network whose architecture inherits the
performance reached by a recent yet popular image-denoising network. To train
this network, a whole simulation framework is built to generate large data sets
according to physics-inspired and statistical models of the pulsar signals and
of the RFI. The relevance of the proposed approach is quantitatively assessed
by conducting extensive experiments. In particular, the results show that the
restored dynamic spectra are sufficiently reliable to estimate pulsar
times-of-arrivals with an accuracy close to the one that would be obtained from
RFI-free signals.
