零样本自监督盲图像去噪的低迹自适应
Low-Trace Adaptation of Zero-shot Self-supervised Blind Image Denoising
AI总结：
提出低迹自适应Noise2Noise（LoTA-N2N）模型，通过迹约束损失弥合自监督与监督去噪差距，实现零样本自监督盲去噪的最优性能。
摘要：
基于深度学习的去噪器一直是图像去噪领域近期发展的焦点。在过去几年中，人们对开发仅需含噪图像、无需干净真实值即可训练的自监督去噪网络的兴趣日益增加。然而，当前自监督方法与监督方法之间仍存在性能差距。此外，这些方法通常依赖于对噪声特性的假设，从而限制了它们在真实场景中的适用性。受Frobenius范数展开性质的启发，我们发现引入迹项可以减少自监督与监督方法之间的优化目标差异，从而提升自监督学习的性能。为利用这一见解，我们提出了一种迹约束损失函数，并设计了低迹自适应Noise2Noise（LoTA-N2N）模型，以弥合自监督与监督学习之间的差距。此外，我们发现若干现有的自监督去噪框架自然成为所提迹约束损失的特例。在自然图像和共聚焦图像数据集上进行的大量实验表明，在不依赖任何噪声假设的情况下，我们的方法在零样本自监督图像去噪方法领域达到了最先进的性能。
英文摘要：
Deep learning-based denoiser has been the focus of recent development on image denoising. In the past few years, there has been increasing interest in developing self-supervised denoising networks that only require noisy images, without the need for clean ground truth for training. However, a performance gap remains between current self-supervised methods and their supervised counterparts. Additionally, these methods commonly depend on assumptions about noise characteristics, thereby constraining their applicability in real-world scenarios. Inspired by the properties of the Frobenius norm expansion, we discover that incorporating a trace term reduces the optimization goal disparity between self-supervised and supervised methods, thereby enhancing the performance of self-supervised learning. To exploit this insight, we propose a trace-constraint loss function and design the low-trace adaptation Noise2Noise (LoTA-N2N) model that bridges the gap between self-supervised and supervised learning. Furthermore, we have discovered that several existing self-supervised denoising frameworks naturally fall within the proposed trace-constraint loss as subcases. Extensive experiments conducted on natural and confocal image datasets indicate that our method achieves state-of-the-art performance within the realm of zero-shot self-supervised image denoising approaches, without relying on any assumptions regarding the noise.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
