DCANet: Dual Convolutional Neural Network with Attention for Image Blind Denoising
Not on the map. Why?
Abstract (arXiv)
Noise removal of images is an essential preprocessing procedure for many computer vision tasks. Currently, many denoising models based on deep neural networks can perform well in removing the noise with known distributions (i.e. the additive Gaussian white noise). However eliminating real noise is still a very challenging task, since real-world noise often does not simply follow one single type of distribution, and the noise may spatially vary. In this paper, we present a new dual convolutional neural network (CNN) with attention for image blind denoising, named as the DCANet. To the best of our knowledge, the proposed DCANet is the first work that integrates both the dual CNN and attention mechanism for image denoising. The DCANet is composed of a noise estimation network, a spatial and channel attention module (SCAM), and a CNN with a dual structure. The noise estimation network is utilized to estimate the spatial distribution and the noise level in an image. The noisy image and its estimated noise are combined as the input of the SCAM, and a dual CNN contains two different branches is designed to learn the complementary features to obtain the denoised image. The experimental results have verified that the proposed DCANet can suppress both synthetic and real noise effectively. The code of DCANet is available at https://github.com/WenCongWu/DCANet.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 43 of its 107 references that are in the map. First posted on arXiv 4 Apr 2023.
Builds on
43 of 107 references.
References · 43
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
- 2016Densely Connected Convolutional Networkscited by 6,720
- 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 5,045
- 2017Squeeze-and-Excitation Networkscited by 4,789
- 2014Going Deeper with Convolutionscited by 4,241
- 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,611
- 2018CBAM: Convolutional Block Attention Modulecited by 2,477
- 2016Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoisingcited by 1,358
- 2015Multi-Scale Context Aggregation by Dilated Convolutionscited by 1,133
- 2021Multi-Stage Progressive Image Restorationcited by 816
31 more
- 2018Residual Dense Network for Image Super-Resolutioncited by 799
- 2017FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoisingcited by 510
- 2017Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolutioncited by 453
- 2017Learning Deep CNN Denoiser Prior for Image Restorationcited by 408
- 2018Toward Convolutional Blind Denoising of Real Photographscited by 274
- 2020Multi-Scale Progressive Fusion Network for Single Image Derainingcited by 260
- 2018Density-aware Single Image De-raining using a Multi-stream Dense Networkcited by 248
- 2019Residual Non-local Attention Networks for Image Restorationcited by 236
- 2017Benchmarking Denoising Algorithms with Real Photographscited by 209
- 2017Attentive Generative Adversarial Network for Raindrop Removal from a Single Imagecited by 204
- 2017Image De-raining Using a Conditional Generative Adversarial Networkcited by 197
- 2015Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restorationcited by 195
- 2019Spatial Attentive Single-Image Deraining with a High Quality Real Rain Datasetcited by 192
- 2019Real Image Denoising with Feature Attentioncited by 176
- 2017Understanding Convolution for Semantic Segmentationcited by 168
- 2016Clearing the Skies: A deep network architecture for single-image rain removalcited by 136
- 2019Variational Denoising Network: Toward Blind Noise Modeling and Removalcited by 117
- 2020Transfer Learning from Synthetic to Real-Noise Denoising with Adaptive Instance Normalizationcited by 92
- 2018Gated Context Aggregation Network for Image Dehazing and Derainingcited by 89
- 2018Attention-guided Unified Network for Panoptic Segmentationcited by 71
- 2018Lightweight Pyramid Networks for Image Derainingcited by 64
- 2018A Trilateral Weighted Sparse Coding Scheme for Real-World Image Denoisingcited by 42
- 2017Multi-channel Weighted Nuclear Norm Minimization for Real Color Image Denoisingcited by 41
- 2022Multi-stage image denoising with the wavelet transformcited by 37
- 2014RENOIR - A Dataset for Real Low-Light Image Noise Reductioncited by 36
- 2018Learning Dual Convolutional Neural Networks for Low-Level Visioncited by 35
- 2019Blind Universal Bayesian Image Denoising with Gaussian Noise Level Learningcited by 31
- 2020Designing and Training of A Dual CNN for Image Denoisingcited by 21
- 2021Deep Universal Blind Image Denoisingcited by 10
- 2022Multi-channel Nuclear Norm Minus Frobenius Norm Minimization for Color Image Denoisingcited by 5
- 2022CFNet: Conditional Filter Learning with Dynamic Noise Estimation for Real Image Denoisingcited by 2
Built on by
Citing papers per year: 2023: 1; 2024: 0; 2025: 0; 2026: 2.
3 papers.