6 citations · 16 across the 5 of their papers we have counts for
5 papers
Two-stage Progressive Residual Dense Attention Network for Image Denoising
Wencong Wu, An Ge, Guannan Lv +3
Deep convolutional neural networks (CNNs) for image denoising can effectively exploit rich hierarchical features and have achieved great success. However, many deep CNN-based denoi…
Dual Residual Attention Network for Image Denoising
Wencong Wu, Shijie Liu, Yi Zhou +2
In image denoising, deep convolutional neural networks (CNNs) can obtain favorable performance on removing spatially invariant noise. However, many of these networks cannot perform…
Image Blind Denoising Using Dual Convolutional Neural Network with Skip Connection
Wencong Wu, Shicheng Liao, Guannan Lv +2
In recent years, deep convolutional neural networks have shown fascinating performance in the field of image denoising. However, deeper network architectures are often accompanied…
DCANet: Dual Convolutional Neural Network with Attention for Image Blind Denoising
Wencong Wu, Guannan Lv, Yingying Duan +3
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 r…
Mirror-Yolo: A Novel Attention Focus, Instance Segmentation and Mirror Detection Model
Fengze Li, Jieming Ma, Zhongbei Tian +4
Mirrors can degrade the performance of computer vision models, but research into detecting them is in the preliminary phase. YOLOv4 achieves phenomenal results in terms of object d…