Recent denoising algorithms based on the "blind-spot" strategy show
impressive blind image denoising performances, without utilizing any external
dataset. While the methods excel in recovering highly contaminated images, we
observe that such algorithms are often less effective under a low-noise or real
noise regime. To address this gap, we propose an alternative denoising strategy
that leverages the architectural inductive bias of implicit neural
representations (INRs), based on our two findings: (1) INR tends to fit the
low-frequency clean image signal faster than the high-frequency noise, and (2)
INR layers that are closer to the output play more critical roles in fitting
higher-frequency parts. Building on these observations, we propose a denoising
algorithm that maximizes the innate denoising capability of INRs by penalizing
the growth of deeper layer weights. We show that our method outperforms
existing zero-shot denoising methods under an extensive set of low-noise or
real-noise scenarios.

介绍了一种新的图像降噪算法，该算法利用了隐式神经表示 (INRs) 在低噪声和真实噪声情况下具有先天降噪能力的特点，通过惩罚更深层次的权重增长来最大化这种天然的降噪能力，比现有的零样本降噪方法更优。

零样本盲图像去噪：基于隐式神经表示的方法

Zero-shot Blind Image Denoising via Implicit Neural Representations

This paper considers the classification of linear subspaces with mismatched
classifiers. In particular, we assume a model where one observes signals in the
presence of isotropic Gaussian noise and the distribution of the signals
conditioned on a given class is Gaussian with a zero mean and a low-rank
covariance matrix. We also assume that the classifier knows only a mismatched
version of the parameters of input distribution in lieu of the true parameters.
By constructing an asymptotic low-noise expansion of an upper bound to the
error probability of such a mismatched classifier, we provide sufficient
conditions for reliable classification in the low-noise regime that are able to
sharply predict the absence of a classification error floor. Such conditions
are a function of the geometry of the true signal distribution, the geometry of
the mismatched signal distributions as well as the interplay between such
geometries, namely, the principal angles and the overlap between the true and
the mismatched signal subspaces. Numerical results demonstrate that our
conditions for reliable classification can sharply predict the behavior of a
mismatched classifier both with synthetic data and in a motion segmentation and
a hand-written digit classification applications.

本文考虑了带有不匹配分类器的线性子空间分类问题，提出了足以可靠分类的充分条件，并通过数值实验验证了这些条件在合成数据和实际应用中的可行性。