The advent of deep learning has brought a revolutionary transformation to image denoising techniques. However, the persistent challenge of acquiring noise-clean pairs for supervised methods in real-world scenarios remains formidable, necessitating the exploration of more practical self-supervised image denoising. This paper focuses on self-supervised image denoising methods that offer effective solutions to address this challenge. Our comprehensive review thoroughly analyzes the latest advancements in self-supervised image denoising approaches, categorizing them into three distinct classes: General methods, Blind Spot Network (BSN)-based methods, and Transformer-based methods. For each class, we provide a concise theoretical analysis along with their practical applications. To assess the effectiveness of these methods, we present both quantitative and qualitative experimental results on various datasets, utilizing classical algorithms as benchmarks. Additionally, we critically discuss the current limitations of these methods and propose promising directions for future research. By offering a detailed overview of recent developments in self-supervised image denoising, this review serves as an invaluable resource for researchers and practitioners in the field, facilitating a deeper understanding of this emerging domain and inspiring further advancements.

翻译：深度学习的出现为图像去噪技术带来了革命性的变革。然而，在现实场景中为监督方法获取噪声-干净图像对的持续挑战仍然严峻，因而有必要探索更实用的自监督图像去噪方法。本文聚焦于能够有效应对该挑战的自监督图像去噪方法。我们的全面综述深入分析了自监督图像去噪方法的最新进展，并将其划分为三个不同的类别：通用方法、基于盲点网络（BSN）的方法和基于Transformer的方法。针对每一类方法，我们提供了简洁的理论分析及其实际应用。为了评估这些方法的有效性，我们在多种数据集上展示了定量和定性的实验结果，并以经典算法作为基准。此外，我们批判性地讨论了这些方法当前存在的局限性，并提出了未来研究的有前景的方向。通过详细概述自监督图像去噪领域的最新发展，本综述为该领域的研究人员和实践者提供了宝贵的资源，有助于加深对这一新兴领域的理解，并激发进一步的创新。