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：深度学习的到来为图像去噪技术带来了革命性的变革。然而，在真实场景中为监督方法获取噪声-干净图像对这一持续挑战仍然艰巨，这促使我们探索更为实用的自监督图像去噪方法。本文聚焦于自监督图像去噪方法，这些方法为应对该挑战提供了有效的解决方案。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，将其分为三个不同的类别：通用方法、基于盲点网络（Blind Spot Network, BSN）的方法以及基于Transformer的方法。针对每一类别，我们给出了简洁的理论分析及其实际应用。为评估这些方法的有效性，我们在多个数据集上展示了定量与定性的实验结果，并采用经典算法作为基准。此外，我们批判性地讨论了这些方法当前的局限性，并提出了未来研究的有前景方向。通过详细概述自监督图像去噪领域的最新发展，本综述为领域内的研究人员和从业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。