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。

基于隐式神经表示的图像去噪器的增强

Boosting of Implicit Neural Representation-based Image Denoiser

The lack of large-scale noisy-clean image pairs restricts supervised
denoising methods' deployment in actual applications. While existing
unsupervised methods are able to learn image denoising without ground-truth
clean images, they either show poor performance or work under impractical
settings (e.g., paired noisy images). In this paper, we present a practical
unsupervised image denoising method to achieve state-of-the-art denoising
performance. Our method only requires single noisy images and a noise model,
which is easily accessible in practical raw image denoising. It performs two
steps iteratively: (1) Constructing a noisier-noisy dataset with random noise
from the noise model; (2) training a model on the noisier-noisy dataset and
using the trained model to refine noisy images to obtain the targets used in
the next round. We further approximate our full iterative method with a fast
algorithm for more efficient training while keeping its original high
performance. Experiments on real-world, synthetic, and correlated noise show
that our proposed unsupervised denoising approach has superior performances
over existing unsupervised methods and competitive performance with supervised
methods. In addition, we argue that existing denoising datasets are of low
quality and contain only a small number of scenes. To evaluate raw image
denoising performance in real-world applications, we build a high-quality raw
image dataset SenseNoise-500 that contains 500 real-life scenes. The dataset
can serve as a strong benchmark for better evaluating raw image denoising. Code
and dataset will be released at this https URL

该论文提出了一种实用的无监督图像去噪方法，其只需要单个有噪声的图象和噪声模型，通过迭代的方式训练模型来实现去噪，得到了目前最先进的去噪效果，并构建了高质量的原始图像数据集 SenseNoise-500，以作为更好评估原始图像去噪性能的强大基准。