Abstract
In this paper, we consider a typical image blind denoising problem, which is to remove unknown noise from noisy
images. As we all know, discriminative learning based
methods, such as DnCNN, can achieve state-of-the-art denoising results, but they are not applicable to this problem
due to the lack of paired training data. To tackle the barrier,
we propose a novel two-step framework. First, a Generative
Adversarial Network (GAN) is trained to estimate the noise
distribution over the input noisy images and to generate
noise samples. Second, the noise patches sampled from the
first step are utilized to construct a paired training dataset,
which is used, in turn, to train a deep Convolutional Neural
Network (CNN) for denoising. Extensive experiments have
been done to demonstrate the superiority of our approach
in image blind denoising