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.

图像去噪是图像处理中最古老且最活跃的研究领域之一。本文提出了一项关于图像去噪的监督学习和无监督学习方法的综述，分类和详细介绍了这一演化过程的主要原则，特别关注最近在监督学习领域的发展。同时，着重考虑了文献中最有效方法的原理和局限性，并突出了许多方法之间的共同特点。最后，我们重点关注大多数监督方法中未能满足的归一化等变特性，即对输入图像进行强度变换或缩放会导致去噪器输出相应地发生变化。