BoostNet: A Boosted Convolutional Neural Network for Image Blind Denoising

Abstract

Deep convolutional neural networks and generative adversarial networks currently attracted the attention of researchers because it is more effective than conventional representation-based methods. However, they have been facing two serious problems in the trade-off between noise removal, artifacts, and preserving low-contrast features and high-frequency details. In particular, deep convolutional neural networks might fail to remove strong noise in regions with higher noise levels while completely erasing low-contrast features and high-frequency details. By contrast, compared with conventional deep convolutional neural networks, generative adversarial networks might be better in balancing between erasing different types of noise and recovering texture details. However, they often generate fake details and unexpected artifacts in the image owing to the instability of their discriminator during training. In this study, we explored an innovative strategy for handling the serious problems of image denoising. With this strategy, we propose a novel boosting generative adversarial network (BoostNet) that not only combines all advantages of a generative adversarial sub-network and a deep convolutional neural network, it also successfully avoids the serious problems caused by the corruption and instability of training. BoostNet is developed by integrating a stand-alone deep convolutional neural network and a robust generative adversarial network into an ensemble network, which can effectively boost the denoising performance. We conducted several experiments using challenging datasets of additive white Gaussian noise and real-world noisy images. The experimental results show that our proposed method is superior to other state-of-the-art denoisers in terms of quantitative metrics and visual quality.