[论文解读] Image Denoising: The Deep Learning Revolution and Beyond -- A Survey Paper --

Michael Elad, Bahjat Kawar+1 more|arXiv (Cornell University)|Jan 9, 2023

Cell Image Analysis Techniques被引用 9

一句话总结

本综述回顾了图像去噪从经典贝叶斯与信号处理方法到现代基于深度学习的方法的演变，并探讨去噪在逆问题和图像合成中的更广泛角色。

ABSTRACT

Image denoising (removal of additive white Gaussian noise from an image) is one of the oldest and most studied problems in image processing. An extensive work over several decades has led to thousands of papers on this subject, and to many well-performing algorithms for this task. Indeed, 10 years ago, these achievements have led some researchers to suspect that "Denoising is Dead", in the sense that all that can be achieved in this domain has already been obtained. However, this turned out to be far from the truth, with the penetration of deep learning (DL) into image processing. The era of DL brought a revolution to image denoising, both by taking the lead in today's ability for noise removal in images, and by broadening the scope of denoising problems being treated. Our paper starts by describing this evolution, highlighting in particular the tension and synergy that exist between classical approaches and modern DL-based alternatives in design of image denoisers. The recent transitions in the field of image denoising go far beyond the ability to design better denoisers. In the 2nd part of this paper we focus on recently discovered abilities and prospects of image denoisers. We expose the possibility of using denoisers to serve other problems, such as regularizing general inverse problems and serving as the prime engine in diffusion-based image synthesis. We also unveil the idea that denoising and other inverse problems might not have a unique solution as common algorithms would have us believe. Instead, we describe constructive ways to produce randomized and diverse high quality results for inverse problems, all fueled by the progress that DL brought to image denoising. This survey paper aims to provide a broad view of the history of image denoising and closely related topics. Our aim is to give a better context to recent discoveries, and to the influence of DL in our domain.

研究动机与目标

• 定义图像去噪问题及其病态性质。
• 回顾从经典时代到深度学习时代的先验和去噪器的发展历史。
• 解释深度学习如何重塑去噪实践及其与人工智能进展的关系。
• 讨论去噪器如何作为正则化逆问题和图像生成/合成的构建模块。

提出的方法

• 给出高斯白噪声 AWGN 去噪问题的形式化及 MMSE/MAP 估计量。
• 追踪图像先验从简单正则化到稀疏性和低秩模型的发展。
• 描述经典去噪技术（如 BM3D、WNNM）及其原理。
• 概述基于深度学习的去噪范式：数据驱动训练、噪声建模和损失设计。
• 讨论 Plug-and-Play 与通过去噪进行正则化的概念以及基于扩散模型的合成。

实验结果

研究问题

• RQ1 在图像去噪中，先验设计如何从经典方法演变到基于学习的方法？
• RQ2 与传统方法相比，深度学习对去噪器的性能和应用范围产生了怎样的影响？
• RQ3 如何将去噪器用作其他逆问题的先验或正则化？
• RQ4 去噪器在随机化、多样化、具有高感知质量的逆问题解和图像合成方面有哪些前景？

主要发现

• 基于深度学习的去噪器在降噪方面领先，优于许多经典方法。
• 去噪器能够作为强大的先验/正则化用于逆问题，启用新的求解策略。
• 去噪器使逆问题能够产生随机化、多样化且具有高感知质量的结果，揭示不确定性。
• 去噪进展与扩散模型的发展和感知质量优化交织在一起。
• 本综述强调去噪中经典先验与基于 AI 的设计之间的协同与张力。