Pattern recognition (psychology)

Noise reduction

Multiview Video Coding

Discriminative model

Noise (video)

Image (mathematics)

Image restoration

Artificial intelligence

Video tracking

Image denoising

Generative adversarial network

Computer science

Video processing

Noise measurement

Video denoising

Computer vision

Convolutional neural network

Image processing

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.

Image Blind Denoising with Generative Adversarial Network Based Noise Modeling