Learning Dual Convolutional Neural Networks for Low-Level Vision
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| Authors | Jin-shan Pan, Sifei Liu, +8 authors Ming-Hsuan Yang |
| Journal/Conference Name | IEEE/CVF Conference on Computer Vision and… |
| Paper Category | ECE |
| Paper 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 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. |
| Date of publication | 2018 |
| Code Programming Language | Python |
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