Learning Dual Convolutional Neural Networks for Low-Level Vision

Jinshan Pan, Sifei Liu, Deqing Sun, Jiawei Zhang, Yang Liu, Jimmy Ren, Zechao Li, Jinhui Tang, Huchuan Lu, Yu-Wing Tai, Ming-Hsuan Yang; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 3070-3079

Abstract


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 with 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.

Related Material


[pdf] [Supp] [arXiv]
[bibtex]
@InProceedings{Pan_2018_CVPR,
author = {Pan, Jinshan and Liu, Sifei and Sun, Deqing and Zhang, Jiawei and Liu, Yang and Ren, Jimmy and Li, Zechao and Tang, Jinhui and Lu, Huchuan and Tai, Yu-Wing and Yang, Ming-Hsuan},
title = {Learning Dual Convolutional Neural Networks for Low-Level Vision},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}