去噪从降噪发展为图像与机器学习的核心工具
Denoising: A Powerful Building-Block for Imaging, Inverse Problems, and Machine Learning
- 将去噪视为通用模块，统一分析其结构与特性
- 揭示去噪在逆问题与机器学习中的核心作用
- 适合研究图像处理与算法设计的学者参考
去噪是消除信号中随机波动以突出本质模式的基本任务，自现代科学兴起以来一直备受关注。近年来，尤其在成像领域，去噪技术已取得显著进展，部分指标接近理论极限。然而，尽管已有数万篇相关论文，去噪在噪声去除之外的广泛应用仍未被充分认识，这主要源于文献庞杂、体系分散，难以形成清晰综述。本文旨在填补这一空白，提出对去噪器的清晰视角，阐明其结构特征与理想性质。强调去噪日益重要的地位，并展示其如何演变为成像、逆问题和机器学习中复杂任务的关键构建模块。尽管历史悠久，去噪仍不断涌现出意想不到且突破性的新应用，进一步巩固其作为科学与工程实践基石的地位。
原文摘要 · Abstract (English)
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 clarifying 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.
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