Research 6 Tencent Youtu Lab 7 UC Merced (a) GT (b) SRCNN [5] (c) VDSR with nearest (d) VDSR with bilinear (e) VDSR with bicubic (f) Ours Figure 1. Visual comparisons of super-resolution results by the VDSR method [17] (×4) with structures recovered by different methods, i.e., nearest neighbor, bilinear, and bicubic upsampling. Residual learning algorithms usually take upsampled image as the base structures and learn the details, the difference between the upsampled and ground truth images. However, residual learning cannot correct low-frequency errors in the structures, e.g., the structure obtained by nearest neighbor interpolation in (c). In contrast, our algorithm is motivated by the decomposition of a signal into structures and details, which involves both structure and detail learning and thus leads to better results. 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 C-NNs. 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.
In 2018, Pan et al proposed a dual convolutional neural network (DualCNN) REF structure consisting of two branches to solve low-level vision problems.
Learning Dual Convolutional Neural Networks for Low-Level Vision
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Computer Science