# Designing and Training of A Dual CNN for Image Denoising

arXiv: https://arxiv.org/abs/2007.03951 · HTML: https://mlsanity.com/paper/2007.03951/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2020.

## Built on by (most cited first, 21 of 21)

- [Multi-stage image denoising with the wavelet transform](https://mlsanity.com/paper/2209.12394/) (2022)
- [Asymmetric CNN for image super-resolution](https://mlsanity.com/paper/2103.13634/) (2021)
- [Mixed Hierarchy Network for Image Restoration](https://mlsanity.com/paper/2302.09554/) (2023)
- [A Comparison of Image Denoising Methods](https://mlsanity.com/paper/2304.08990/) (2023)
- [Learning Multi-scale Spatial-frequency Features for Image Denoising](https://mlsanity.com/paper/2506.16307/) (2025)
- [Dual Residual Attention Network for Image Denoising](https://mlsanity.com/paper/2305.04269/) (2023)
- [A cross Transformer for image denoising](https://mlsanity.com/paper/2310.10408/) (2023)
- [Image Blind Denoising Using Dual Convolutional Neural Network with Skip Connection](https://mlsanity.com/paper/2304.01620/) (2023)
- [Test-time Adaptation for Real Image Denoising via Meta-transfer Learning](https://mlsanity.com/paper/2207.02066/) (2022)
- [DCANet: Dual Convolutional Neural Network with Attention for Image Blind Denoising](https://mlsanity.com/paper/2304.01498/) (2023)
- [A Mountain-Shaped Single-Stage Network for Accurate Image Restoration](https://mlsanity.com/paper/2305.05146/) (2023)
- [NODE-ImgNet: a PDE-informed effective and robust model for image denoising](https://mlsanity.com/paper/2305.11049/) (2023)
- [A Multi-Head Convolutional Neural Network With Multi-path Attention improves Image Denoising](https://mlsanity.com/paper/2204.12736/) (2022)
- [DnSwin: Toward Real-World Denoising via Continuous Wavelet Sliding-Transformer](https://mlsanity.com/paper/2207.13861/) (2022)
- [CFNet: Conditional Filter Learning with Dynamic Noise Estimation for Real Image Denoising](https://mlsanity.com/paper/2211.14576/) (2022)
- [Touchless Palmprint Recognition based on 3D Gabor Template and Block Feature Refinement](https://mlsanity.com/paper/2103.02167/) (2021)
- [Considering Image Information and Self-similarity: A Compositional Denoising Network](https://mlsanity.com/paper/2209.06417/) (2022)
- [Adaptive aggregation of Monte Carlo augmented decomposed filters for efficient group-equivariant convolutional neural network](https://mlsanity.com/paper/2305.10110/) (2023)
- [Exploring Richer and More Accurate Information via Frequency Selection for Image Restoration](https://mlsanity.com/paper/2407.08950/) (2024)
- [A Novel Truncated Norm Regularization Method for Multi-channel Color Image Denoising](https://mlsanity.com/paper/2307.07932/) (2023)
- [Two-stage Progressive Residual Dense Attention Network for Image Denoising](https://mlsanity.com/paper/2401.02831/) (2024)

## Abstract (arXiv)

Deep convolutional neural networks (CNNs) for image denoising have recently attracted increasing research interest. However, plain networks cannot recover fine details for a complex task, such as real noisy images. In this paper, we propsoed a Dual denoising Network (DudeNet) to recover a clean image. Specifically, DudeNet consists of four modules: a feature extraction block, an enhancement block, a compression block, and a reconstruction block. The feature extraction block with a sparse machanism extracts global and local features via two sub-networks. The enhancement block gathers and fuses the global and local features to provide complementary information for the latter network. The compression block refines the extracted information and compresses the network. Finally, the reconstruction block is utilized to reconstruct a denoised image. The DudeNet has the following advantages: (1) The dual networks with a parse mechanism can extract complementary features to enhance the generalized ability of denoiser. (2) Fusing global and local features can extract salient features to recover fine details for complex noisy images. (3) A Small-size filter is used to reduce the complexity of denoiser. Extensive experiments demonstrate the superiority of DudeNet over existing current state-of-the-art denoising methods.