177 citations · 267 across the 12 of their papers we have counts for
12 papers
A cross Transformer for image denoising
Chunwei Tian, Menghua Zheng, Wangmeng Zuo +3
Deep convolutional neural networks (CNNs) depend on feedforward and feedback ways to obtain good performance in image denoising. However, how to obtain effective structural informa…
A Distributed Computation Model Based on Federated Learning Integrates Heterogeneous models and Consortium Blockchain for Solving Time-Varying Problems
Zhihao Hao, Guancheng Wang, Chunwei Tian +1
The recurrent neural network has been greatly developed for effectively solving time-varying problems corresponding to complex environments. However, limited by the way of centrali…
Multi-stage image denoising with the wavelet transform
Chunwei Tian, Menghua Zheng, Wangmeng Zuo +3
Deep convolutional neural networks (CNNs) are used for image denoising via automatically mining accurate structure information. However, most of existing CNNs depend on enlarging d…
A heterogeneous group CNN for image super-resolution
Chunwei Tian, Yanning Zhang, Wangmeng Zuo +3
Convolutional neural networks (CNNs) have obtained remarkable performance via deep architectures. However, these CNNs often achieve poor robustness for image super-resolution (SR)…
Image Super-resolution with An Enhanced Group Convolutional Neural Network
Chunwei Tian, Yixuan Yuan, Shichao Zhang +3
CNNs with strong learning abilities are widely chosen to resolve super-resolution problem. However, CNNs depend on deeper network architectures to improve performance of image supe…
Generative Adversarial Networks for Image Super-Resolution: A Survey
Ziang Wu, Xuanyu Zhang, Yinbo Yu +3
Single image super-resolution (SISR) has played an important role in the field of image processing. Recent generative adversarial networks (GANs) can achieve excellent results on l…
Asymmetric CNN for image super-resolution
Chunwei Tian, Yong Xu, Wangmeng Zuo +2
Deep convolutional neural networks (CNNs) have been widely applied for low-level vision over the past five years. According to nature of different applications, designing appropria…
Lightweight image super-resolution with enhanced CNN
Chunwei Tian, Ruibin Zhuge, Zhihao Wu +4
Deep convolutional neural networks (CNNs) with strong expressive ability have achieved impressive performances on single image super-resolution (SISR). However, their excessive amo…
Designing and Training of A Dual CNN for Image Denoising
Chunwei Tian, Yong Xu, Wangmeng Zuo +3
Deep convolutional neural networks (CNNs) for image denoising have recently attracted increasing research interest. However, plain networks cannot recover fine details for a comple…
Deep Learning on Image Denoising: An overview
Chunwei Tian, Lunke Fei, Wenxian Zheng +3
Deep learning techniques have received much attention in the area of image denoising. However, there are substantial differences in the various types of deep learning methods deali…
Deep Learning for Image Denoising: A Survey
Chunwei Tian, Yong Xu, Lunke Fei +1
Since the proposal of big data analysis and Graphic Processing Unit (GPU), the deep learning technology has received a great deal of attention and has been widely applied in the fi…
Enhanced CNN for image denoising
Chunwei Tian, Yong Xu, Lunke Fei +3
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)…