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.

翻译：隐式神经表示（INR）已成为无监督图像去噪的有效方法。然而，INR模型通常过度参数化；因此，这些模型在学习过程中容易过拟合，导致结果次优，甚至产生噪声。为解决这一问题，我们提出了一种用于图像去噪中INR模型正则化的通用策略。具体而言，我们提出在学习过程中迭代地用预测值与监督信号的均值替换监督信号。我们从理论上证明，这种简单的迭代替换可以逐步提高监督信号的信噪比，从而在学习过程中使INR模型受益。实验结果表明，所提方法能有效正则化INR模型，缓解过拟合并提升图像去噪性能。