Enhanced CNN for image denoising
This addresses image denoising for computer vision applications, but it appears incremental as it builds on existing CNN techniques.
The authors tackled image denoising by proposing ECNDNet, a CNN-based method using residual learning, batch normalization, and dilated convolutions to improve training and performance, and it outperformed state-of-the-art methods.
Owing to flexible architectures of deep convolutional neural networks (CNNs), 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.