Toward Convolutional Blind Denoising of Real Photographs

While deep convolutional neural networks (CNNs) have achieved impressive success in image denoising with additive white Gaussian noise (AWGN), their performance remains limited on real-world noisy photographs. The main reason is that their learned models are easy to overfit on the simplified AWGN model which deviates severely from the complicated real-world noise model. In order to improve the generalization ability of deep CNN denoisers, we suggest training a convolutional blind denoising network (CBDNet) with more realistic noise model and real-world noisy-clean image pairs. On the one hand, both signal-dependent noise and in-camera signal processing pipeline is considered to synthesize realistic noisy images. On the other hand, real-world noisy photographs and their nearly noise-free counterparts are also included to train our CBDNet. To further provide an interactive strategy to rectify denoising result conveniently, a noise estimation subnetwork with asymmetric learning to suppress under-estimation of noise level is embedded into CBDNet. Extensive experimental results on three datasets of real-world noisy photographs clearly demonstrate the superior performance of CBDNet over state-of-the-arts in terms of quantitative met- rics and visual quality. The code has been made available at https://github.com/GuoShi28/CBDNet.

1. Introduction

Image denoising is an essential and fundamental problem in low-level vision and image processing. With decades of studies, numerous promising approaches [3], [12], [17], [53], [11], [61] have been developed and near-optimal performance [8], [31], [50] has been achieved for the removal of additive white Gaussian noise (AWGN). However, in real camera system, image noise comes from multiple sources (e.g., dark current noise, short noise, and thermal noise) and is further affected by in-camera processing (ISP) pipeline (e.g., demosaicing, Gamma correction, and compression). All these make real noise much more different from AWGN, and blind denoising of real-world noisy photographs remains a challenging issue.