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.

深度学习为图像去噪技术带来了革命性的转变，本文聚焦于自监督图像去噪方法，对最新的方法进行了全面的分析和分类，并提供了理论分析和实际应用，同时讨论了这些方法的局限性并提出了未来研究的方向。