ML Anthology

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
CVPR 2018
doi:10.1109/CVPR.2018.00324 /cvpr/2018/pan2018cvpr-learning/

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.

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Cite

Text

Pan et al. "Learning Dual Convolutional Neural Networks for Low-Level Vision." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018. doi:10.1109/CVPR.2018.00324

Markdown

[Pan et al. "Learning Dual Convolutional Neural Networks for Low-Level Vision." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.](https://mlanthology.org/cvpr/2018/pan2018cvpr-learning/) doi:10.1109/CVPR.2018.00324

BibTeX

@inproceedings{pan2018cvpr-learning,
  title     = {{Learning Dual Convolutional Neural Networks for Low-Level Vision}},
  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},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2018},
  doi       = {10.1109/CVPR.2018.00324},
  url       = {https://mlanthology.org/cvpr/2018/pan2018cvpr-learning/}
}