Image denoising is a representative image restoration task in computer
vision. Recent progress of image denoising from only noisy images has attracted
much attention. Deep image prior (DIP) demonstrated successful image denoising
from only a noisy image by inductive bias of convolutional neural network
architectures without any pre-training. The major challenge of DIP based image
denoising is that DIP would completely recover the original noisy image unless
applying early stopping. For early stopping without a ground-truth clean image,
we propose to monitor JPEG file size of the recovered image during optimization
as a proxy metric of noise levels in the recovered image. Our experiments show
that the compressed image file size works as an effective metric for early
stopping.

图像去噪是计算机视觉领域中代表性的图像恢复任务。最近关于只使用噪声图像进行图像去噪的研究引起了广泛关注。深度图像先验 (DIP) 通过卷积神经网络结构的归纳偏置实现了成功的只使用噪声图像进行图像去噪，而无需任何预训练。DIP 图像去噪的主要挑战是，除非应用早停止技术，否则 DIP 将完全恢复原始噪声图像。为了在没有原始清晰图像的情况下进行早停止，我们提出通过优化过程中监视恢复图像的 JPEG 文件大小作为噪声水平的代理度量标准。实验证明，压缩图像文件大小可以作为早停止的有效指标。