Attention-guided CNN for image denoising

By Chunwei Tian, Yong Xu, Zuoyong Li, Wangmeng Zuo, Lunke Fei, Hong Liu. Neural Networks, 2020

620 citations recorded by the source corpus.

Deep convolutional neural networks (CNNs) have attracted considerable interest in low-level computer vision. Researches are usually devoted to improving the performance via very deep CNNs. However, as the depth increases, influences of the shallow layers on deep layers are weakened. Inspired by the fact, we propose an attention-guided denoising convolutional neural network (ADNet), mainly including a sparse block (SB), a feature enhancement block (FEB), an attention block (AB) and a reconstruction block (RB) for image denoising. Specifically, the SB makes a tradeoff between performance and efficiency by using dilated and common convolutions to remove the noise. The FEB integrates global and local features information via a long path to enhance the expressive ability of the denoising model. The AB is used to finely extract the noise information hidden in the complex background, which is very effective for complex noisy images, especially real noisy images and bind denoising. Also, the FEB is integrated with the AB to improve the efficiency and reduce the complexity for training a denoising model. Finally, a RB aims to construct the clean image through the obtained noise mapping and the given noisy image. Additionally, comprehensive experiments show that the proposed ADNet performs very well in three tasks (i.e. synthetic and real noisy images, and blind denoising) in terms of both quantitative and qualitative evaluations. The code of ADNet is accessible at https://github.com/hellloxiaotian/ADNet.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/3e7448c2bbfe2fd9d56c972ec111776d451967e5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Yunhe Wang
2. Yong Xu
3. Chunwei Tian
4. Dacheng Tao
5. Chunjing Xu
6. Lunke Fei
7. Chia-Wen Lin
8. Wenxian Zheng

In Media Technology

1. Chunwei Tian
2. Yong Xu
3. Lunke Fei
4. Chia-Wen Lin
5. Wenxian Zheng
6. Zhijin Qin
7. Huiqiang Xie
8. Chunjing Xu

Citations in the corpus

• A Lite Distributed Semantic Communication System for Internet of Things (2020, methodology): “Here, we exploit attention-guided denoising convolutional neural network (ADNet) [39] to refine CSI.”
• Deep Learning on Image Denoising: An overview (2019, positive): “[206] CNN Gaussianimagedenoising,blinddenoisingandrealnoisyimagedenoising CNNwithattentionmechanismandsparsemethodforimagedenoising”
• Pre-Trained Image Processing Transformer (2020, background): “A various excellent works are also proposed for the other tasks, such as denoising [60, 31, 36, 42, 24], dehazing [6, 43, 74, 71], deraining [35, 69, 55, 29, 65, 44], and debluring [59, 50, 23, 10].”