TY - GEN
T1 - Learning Dual Convolutional Neural Networks for Low-Level Vision
AU - Pan, Jinshan
AU - Liu, Sifei
AU - Sun, Deqing
AU - Zhang, Jiawei
AU - Liu, Yang
AU - Ren, Jimmy
AU - Li, Zechao
AU - Tang, Jinhui
AU - Lu, Huchuan
AU - Tai, Yu Wing
AU - Yang, Ming Hsuan
N1 - Publisher Copyright:
© 2018 IEEE.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2018/12/14
Y1 - 2018/12/14
N2 - In this paper, we propose a general dual convolutional neural network (DualCNN) for low-level vision problems, e.g., super-resolution, edge-preserving filtering, deraining and dehazing. These problems usually involve the estimation of two components of the target signals: structures and details. Motivated by this, our proposed DualCNN consists of two parallel branches, which respectively recovers the structures and details in an end-to-end manner. The recovered structures and details can generate the target signals according to the formation model for each particular application. The DualCNN is a flexible framework for low-level vision tasks and can be easily incorporated into existing CNNs. Experimental results show that the DualCNN can be effectively applied to numerous low-level vision tasks with favorable performance against the state-of-the-art methods.
AB - In this paper, we propose a general dual convolutional neural network (DualCNN) for low-level vision problems, e.g., super-resolution, edge-preserving filtering, deraining and dehazing. These problems usually involve the estimation of two components of the target signals: structures and details. Motivated by this, our proposed DualCNN consists of two parallel branches, which respectively recovers the structures and details in an end-to-end manner. The recovered structures and details can generate the target signals according to the formation model for each particular application. The DualCNN is a flexible framework for low-level vision tasks and can be easily incorporated into existing CNNs. Experimental results show that the DualCNN can be effectively applied to numerous low-level vision tasks with favorable performance against the state-of-the-art methods.
UR - http://www.scopus.com/inward/record.url?scp=85053748887&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85053748887&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2018.00324
DO - 10.1109/CVPR.2018.00324
M3 - Conference contribution
AN - SCOPUS:85053748887
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SP - 3070
EP - 3079
BT - Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018
PB - IEEE Computer Society
T2 - 31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018
Y2 - 18 June 2018 through 22 June 2018
ER -