# Enhanced CNN for image denoising

arXiv: https://arxiv.org/abs/1810.11834 · HTML: https://mlsanity.com/paper/1810.11834/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2018.

## Built on by (most cited first, 6 of 6)

- [Evaluating Similitude and Robustness of Deep Image Denoising Models via Adversarial Attack](https://mlsanity.com/paper/2306.16050/) (2023)
- [A cross Transformer for image denoising](https://mlsanity.com/paper/2310.10408/) (2023)
- [NeighCNN: A CNN based SAR Speckle Reduction using Feature preserving Loss Function](https://mlsanity.com/paper/2108.11573/) (2021)
- [CASSOD-Net: Cascaded and Separable Structures of Dilated Convolution for Embedded Vision Systems and Applications](https://mlsanity.com/paper/2104.14126/) (2021)
- [Single image dehazing via combining the prior knowledge and CNNs](https://mlsanity.com/paper/2111.05701/) (2021)
- [Training Implicit Networks for Image Deblurring using Jacobian-Free Backpropagation](https://mlsanity.com/paper/2402.02065/) (2024)

## Abstract (arXiv)

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.