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。