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