  Deep Convolutional Neural Networks (CNNs) have been successfully used in many
low-level vision problems like image denoising. Although the conditional image
generation techniques have led to large improvements in this task, there has
been little effort in providing conditional generative adversarial networks
(cGAN)[42] with an explicit way of understanding the image noise for
object-independent denoising reliable for real-world applications. The task of
leveraging structures in the target space is unstable due to the complexity of
patterns in natural scenes, so the presence of unnatural artifacts or
over-smoothed image areas cannot be avoided. To fill the gap, in this work we
introduce the idea of a cGAN which explicitly leverages structure in the image
noise space. By learning directly a low dimensional manifold of the image
noise, the generator promotes the removal from the noisy image only that
information which spans this manifold. This idea brings many advantages while
it can be appended at the end of any denoiser to significantly improve its
performance. Based on our experiments, our model substantially outperforms
existing state-of-the-art architectures, resulting in denoised images with less
oversmoothing and better detail.
