论文标题
未配对的图像denoing
Unpaired Image Denoising
论文作者
论文摘要
图像处理中的深度学习方法主要诉诸监督学习。大多数用于图像降级的方法也不例外，因此需要嘈杂和相应的清洁图像对。直到最近，诸如Noige2Void之类的方法出现了，深层神经网络才能学会仅从嘈杂的图像中降低。但是，当实际上可用的任何嘈杂图像直接对应的清洁图像时，就可以改进空间，因为这些干净的图像包含有用的信息，这些信息完全不受监督的方法不会利用。在本文中，我们提出了一种在这种情况下进行图像denoing的方法。首先，我们使用基于流的生成模型从干净的图像中学习先验。然后，我们使用它来训练Denoising网络，而无需任何干净的目标。我们通过广泛的实验和比较来证明我们方法的功效。
Deep learning approaches in image processing predominantly resort to supervised learning. A majority of methods for image denoising are no exception to this rule and hence demand pairs of noisy and corresponding clean images. Only recently has there been the emergence of methods such as Noise2Void, where a deep neural network learns to denoise solely from noisy images. However, when clean images that do not directly correspond to any of the noisy images are actually available, there is room for improvement as these clean images contain useful information that fully unsupervised methods do not exploit. In this paper, we propose a method for image denoising in this setting. First, we use a flow-based generative model to learn a prior from clean images. We then use it to train a denoising network without the need for any clean targets. We demonstrate the efficacy of our method through extensive experiments and comparisons.
