Owing to the flexible architectures of deep convolutional neural networks(CNNs)are successfully used for image denoising.However,they suffer from the following drawbacks:(i)deep network architecture is very difficult ...Owing to the flexible architectures of deep convolutional neural networks(CNNs)are successfully used for image denoising.However,they suffer from the following drawbacks:(i)deep network architecture is very difficult to train.(ii)Deeper networks face the challenge of performance saturation.In this study,the authors propose a novel method called enhanced convolutional neural denoising network(ECNDNet).Specifically,they use residual learning and batch normalisation techniques to address the problem of training difficulties and accelerate the convergence of the network.In addition,dilated convolutions are used in the proposed network to enlarge the context information and reduce the computational cost.Extensive experiments demonstrate that the ECNDNet outperforms the state-of-the-art methods for image denoising.展开更多
文摘Owing to the flexible architectures of deep convolutional neural networks(CNNs)are successfully used for image denoising.However,they suffer from the following drawbacks:(i)deep network architecture is very difficult to train.(ii)Deeper networks face the challenge of performance saturation.In this study,the authors propose a novel method called enhanced convolutional neural denoising network(ECNDNet).Specifically,they use residual learning and batch normalisation techniques to address the problem of training difficulties and accelerate the convergence of the network.In addition,dilated convolutions are used in the proposed network to enlarge the context information and reduce the computational cost.Extensive experiments demonstrate that the ECNDNet outperforms the state-of-the-art methods for image denoising.
