Denoising, the process of reducing random fluctuations in a signal to emphasize essential patterns, has been a fundamental problem of interest since the dawn of modern scientific inquiry. Recent denoising techniques, particularly in imaging, have achieved remarkable success, nearing theoretical limits by some measures. Yet, despite tens of thousands of research papers, the wide-ranging applications of denoising beyond noise removal have not been fully recognized. This is partly due to the vast and diverse literature, making a clear overview challenging. This paper aims to address this gap. We present a comprehensive perspective on denoisers, their structure, and desired properties. We emphasize the increasing importance of denoising and showcase its evolution into an essential building block for complex tasks in imaging, inverse problems, and machine learning. Despite its long history, the community continues to uncover unexpected and groundbreaking uses for denoising, further solidifying its place as a cornerstone of scientific and engineering practice.

本文解决了去噪技术应用范围未被充分认识的问题。通过提供全面的去噪器结构和属性视角，强调去噪在成像、逆问题和机器学习等复杂任务中的日益重要性。研究发现，去噪不仅限于噪声去除，其潜在用途正在不断被发现，进一步巩固了其在科学和工程实践中的重要地位。

去噪：成像、逆问题和机器学习的重要组成部分