Parallel Feature Pyramid Network for Image Denoising

Sung-Jin Cho, Kwang-Hyun Uhm, Seung-Wook Kim, Seo-Won Ji, Sung-Jea Ko

Abstract: Image denoising is a classical and essential task in consumer electronics equipped with cameras. Recently, the convolutional neural network (CNN)-based denoising methods have been widely studied. These methods adopt single-scale features to separate image structures from the noisy observation. Single-scale features, however, have limitation in covering the full characteristics of image structures at different scales. In this paper, we propose a novel denoising network that makes use of the multi-scale feature pyramid where each feature map represents the characteristics of image structure at different scales. We then combine these multi-scale features to obtain the contextual information and utilize it to effectively generate clear denoised results. Experimental results show that our network achieves superior performance to other conventional methods.