Abstract
We tackle a challenging blind image denoising problem, in which only single noisy images are available for training a denoiser and no information about noise is known, except for it being zero-mean, additive, and independent of the clean image. In such a setting, which often occurs in practice, it is not possible to train a denoiser with the standard discriminative training or with the recently developed Noise2Noise (N2N) training; the former requires the underlying clean image for the given noisy image, and the latter requires two independently realized noisy image pair for a clean image. To that end, we propose GAN2GAN (Generated-Artificial-Noise to Generated-Artificial-Noise) method that can first learn to generate synthetic noisy image pairs that simulate independent realizations of the noise in the given images, then carry out the N2N training of a denoiser with those synthetically generated noisy image pairs. Our method consists of three parts: extracting smooth noisy patches to learn the noise distribution in the given images, training a generative model to synthesize the noisy image pairs, and devising an iterative N2N training of a denoiser. In results, we show the denoiser trained with our GAN2GAN, solely based on single noisy images, achieves an impressive denoising performance, almost approaching the performance of the standard discriminatively-trained or N2N-trained models that have more information than ours, and significantly outperforming the recent baselines for the same setting.
Abstract (translated)
我们解决了一个具有挑战性的盲图像去噪问题，在这个问题中，只有单个噪声图像可用于训练去噪器，并且除了零均值、加性和独立于干净图像之外，没有关于噪声的信息。在实际中经常出现的这种情况下，不可能用标准鉴别训练或最近开发的噪声2n训练来训练一个去噪器；前者要求给定的噪声图像具有底层的干净图像，后者要求两个独立实现的噪声图像对作为一个干净的图像。e.为此，我们提出了一种Gan2gan（生成人工噪声到生成人工噪声）方法，该方法首先学习生成模拟给定图像中噪声独立实现的合成噪声图像对，然后用这些合成噪声图像对对进行去噪器的N2n训练。该方法由三个部分组成：提取平滑的噪声块来了解给定图像中的噪声分布，训练生成模型来合成噪声图像对，设计一个去噪器的迭代N2N训练。结果表明，用我们的Gan2Gan训练的去噪器完全基于单个噪声图像，达到了令人印象深刻的去噪性能，几乎接近标准的有区别训练或N2N训练的模型的性能，这些模型比我们的模型具有更多的信息，并且显著优于同一设置的最近基线。NG。
URL
https://arxiv.org/abs/1905.10488
