Boosting of Implicit Neural Representation-based Image Denoiser
Zipei Yan Zhengji Liu Jizhou Li
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.