  Image denoising is probably the oldest and still one of the most active
research topic in image processing. Many methodological concepts have been
introduced in the past decades and have improved performances significantly in
recent years, especially with the emergence of convolutional neural networks
and supervised deep learning. In this paper, we propose a survey of guided tour
of supervised and unsupervised learning methods for image denoising,
classifying the main principles elaborated during this evolution, with a
particular concern given to recent developments in supervised learning. It is
conceived as a tutorial organizing in a comprehensive framework current
approaches. We give insights on the rationales and limitations of the most
performant methods in the literature, and we highlight the common features
between many of them. Finally, we focus on on the normalization equivariance
properties that is surprisingly not guaranteed with most of supervised methods.
It is of paramount importance that intensity shifting or scaling applied to the
input image results in a corresponding change in the denoiser output.
