Abstract
\beginabstract Deep convolutional neural networks (CNNs) have been demonstrated to be effective for singe-image super-resolution (SISR) recently. Inspired by Chen et al. \citechen2017dual, we propose a novel method for SISR by introducing dual path connections into a deep convolutional neural network, we call it SRDPN. SRDPN consists of three parts, which are feature extraction block, multiple stacked dual path blocks and reconstruction block. Each dual path block is made of one transition unit and several cascading dual path units. Dual path unit, the core component of the proposed SRDRN, is a specially designed network unit which uses both residual connection and dense connections for convolution layer to exploit common features and explore new features layer-wise. The transition unit in each dual path block is used to fuse the residual and dense features in previous dual path block to keep computation and memory cost under control. Finally, we concatenate outputs of all the dual path blocks for reconstruction of a residual between high-resolution (HR) image and low-resolution (LR) image, both making information forward-propagation direct and alleviating gradient vanishing/exploding problem. Experiments show the proposed SRDPN has superior performance over the state-of-the-art methods. \endabstract
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call