  Deep image denoisers achieve state-of-the-art results but with a hidden cost.
As witnessed in recent literature, these deep networks are capable of
overfitting their training distributions, causing inaccurate hallucinations to
be added to the output and generalizing poorly to varying data. For better
control and interpretability over a deep denoiser, we propose a novel framework
exploiting a denoising network. We call it controllable confidence-based image
denoising (CCID). In this framework, we exploit the outputs of a deep denoising
network alongside an image convolved with a reliable filter. Such a filter can
be a simple convolution kernel which does not risk adding hallucinated
information. We propose to fuse the two components with a frequency-domain
approach that takes into account the reliability of the deep network outputs.
With our framework, the user can control the fusion of the two components in
the frequency domain. We also provide a user-friendly map estimating spatially
the confidence in the output that potentially contains network hallucination.
Results show that our CCID not only provides more interpretability and control,
but can even outperform both the quantitative performance of the deep denoiser
and that of the reliable filter, especially when the test data diverge from the
training data.
