Dual Adversarial Network: Toward Real-world Noise Removal and Noise Generation
Real-world image noise removal is a long-standing yet very challenging task in computer vision. The success of deep neural network in denoising stimulates the research of noise generation, aiming at synthesizing more pairs of clean-noisy images to facilitate the training of deep. In this work, we propose a novel unified framework to simultaneously deal with the noise removal and noise generation tasks. Instead of only inferring the posterior distribution of the latent clean image conditioned on the observed noisy image in traditional MAP framework, it learns the joint distribution of the clean-noisy image pairs. Specifically, we approximate the joint distribution with two different factorized forms, which can be formulated as a denoiser mapping the noisy image to the clean one and a generator mapping the clean to the noisy one. The learned joint distribution implicitly contains all the information between the noisy and clean images, avoiding the necessity of manually designing the image priors and noise assumptions as traditional. Besides, the performance of our denoiser can be further improved by augmenting the original training dataset with the learned generator. Moreover, we propose two metrics to assess the quality of the generated noisy image, for which, to the best of our knowledge, such metrics are first proposed along this research line. Extensive experiments have conducted to demonstrate the superiority of our method over the state-of-the-arts in terms of both the real noise removal and noise generation tasks."