探究变性条件下的自监督图像去噪
Investigating Self-Supervised Image Denoising with Denaturation
AI总结：
本文通过理论分析和数值实验，深入探究了使用变性数据的自监督图像去噪算法，证明其能找到总体风险下的理想解，且经验性能与理论一致，为未来改进提供了见解。
摘要：
在存在变性（denaturation）的噪声数据条件下，用于图像去噪问题的自监督学习是机器学习中的一种关键方法。然而，对于使用变性数据的方法的性能，目前缺乏理论上的理解。为了更深入地理解该方法，本文通过理论分析和数值实验，深入分析了一种使用变性数据的自监督去噪算法。通过理论分析，我们论证了该算法能够找到关于总体风险（population risk）的优化问题的理想解，而关于经验风险（empirical risk）的保证则取决于去噪任务在变性程度方面的难度。我们还进行了若干实验，以考察一种扩展算法在实际中的性能。结果表明，使用变性图像训练的算法是有效的，且经验性能与理论结果一致。这些结果为未来进一步改进使用变性数据的自监督图像去噪提供了若干见解。
英文摘要：
Self-supervised learning for image denoising problems in the presence of denaturation for noisy data is a crucial approach in machine learning. However, theoretical understanding of the performance of the approach that uses denatured data is lacking. To provide better understanding of the approach, in this paper, we analyze a self-supervised denoising algorithm that uses denatured data in depth through theoretical analysis and numerical experiments. Through the theoretical analysis, we discuss that the algorithm finds desired solutions to the optimization problem with the population risk, while the guarantee for the empirical risk depends on the hardness of the denoising task in terms of denaturation levels. We also conduct several experiments to investigate the performance of an extended algorithm in practice. The results indicate that the algorithm training with denatured images works, and the empirical performance aligns with the theoretical results. These results suggest several insights for further improvement of self-supervised image denoising that uses denatured data in future directions.
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