去噪:成像、逆问题和机器学习的强大构建模块

Peyman Milanfar, Mauricio Delbracio
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引用次数: 0

摘要

去噪,即减少信号中的随机波动以强调基本模式的过程,自现代科学探索诞生以来,一直是人们感兴趣的基本问题。最近的去噪技术,尤其是成像技术,已经取得了显著的成就,某些指标已经接近理论极限。然而,尽管有数以万计的研究论文,去噪技术在除噪之外的广泛应用仍未得到充分认识。这部分是由于文献浩如烟海,种类繁多,因此很难对其进行清晰的概述。本文旨在填补这一空白。我们从一个全面的角度介绍了去噪器、其结构和所需特性。我们强调了去噪技术日益增长的重要性,并展示了去噪技术在成像、逆问题和机器学习等复杂任务中的重要作用。尽管去噪技术历史悠久,但业界仍在不断发现其意想不到的开创性用途,进一步巩固了其在科学和工程实践中的基石地位。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Denoising: A Powerful Building-Block for Imaging, Inverse Problems, and Machine Learning
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.
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