Dual Path Denoising Network for Real Photographic Noise

This letter presents a convolutional neural network (CNN) for image denoising, especially for the reduction of real noises. As a network topology, we adopt the dual path network (DPN) that combines the advantages of residual and densely connected networks. Using the DPN as a basic building block, we design a network that connects the DPN in dual path again with an attention mechanism. For efficient denoising of real noise images, we build a training set where noisy images are obtained from a heteroscedastic Gaussian noise model and in-camera pipeline. In addition, we augment the synthetic training set with a relatively small number of real noise data. In the experiments, the proposed method is shown to provide state-of-the-art performance in reducing both synthetic and real noises.