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.

翻译：图像去噪可能是图像处理领域中历史最悠久且仍最活跃的研究方向之一。过去几十年中，许多方法论概念被提出，尤其是卷积神经网络和有监督深度学习的出现，使得近年性能显著提升。本文对图像去噪的有监督与无监督学习方法进行综述性导览，系统梳理该领域发展过程中形成的主要原理，并重点关注有监督学习的最新进展。我们将现有方法组织成一个全面的框架进行教程式阐述，深入解析文献中性能最优秀方法的理论依据与局限性，同时揭示众多方法间的共性特征。最后，我们聚焦于归一化等变性质——这一大部分有监督方法意外缺失的特性。至关重要的是，输入图像的强度平移或缩放应使去噪器输出产生相应变化。