Implicit Neural Representation (INR) has emerged as an effective method for
unsupervised image denoising. However, INR models are typically
overparameterized; consequently, these models are prone to overfitting during
learning, resulting in suboptimal results, even noisy ones. To tackle this
problem, we propose a general recipe for regularizing INR models in image
denoising. In detail, we propose to iteratively substitute the supervision
signal with the mean value derived from both the prediction and supervision
signal during the learning process. We theoretically prove that such a simple
iterative substitute can gradually enhance the signal-to-noise ratio of the
supervision signal, thereby benefiting INR models during the learning process.
Our experimental results demonstrate that INR models can be effectively
regularized by the proposed approach, relieving overfitting and boosting image
denoising performance.

通过在学习过程中采用来自预测和监督信号的均值作为监督信号的替代，我们提出了一种用于正则化隐式神经表示模型（INR）在图像去噪中的通用方法，从理论上证明这样的简单迭代替代可以逐渐增强监督信号的信噪比，从而有利于 INR 模型的学习过程，实验证明采用提出的方法可以有效地正则化 INR 模型，缓解过拟合，并提高图像去噪性能。