  Image denoising has recently taken a leap forward due to machine learning.
However, image denoisers, both expert-based and learning-based, are mostly
tested on well-behaved generated noises (usually Gaussian) rather than on
real-life noises, making performance comparisons difficult in real-world
conditions. This is especially true for learning-based denoisers which
performance depends on training data. Hence, choosing which method to use for a
specific denoising problem is difficult.
  This paper proposes a comparative study of existing denoisers, as well as an
extensible open tool that makes it possible to reproduce and extend the study.
MWCNN is shown to outperform other methods when trained for a real-world image
interception noise, and additionally is the second least compute hungry of the
tested methods. To evaluate the robustness of conclusions, three test sets are
compared. A Kendall's Tau correlation of only 60% is obtained on methods
ranking between noise types, demonstrating the need for a benchmarking tool.
