Single Image Super Resolution Based on Deep Residual Network via Lateral Modules

Recently, convolutional neural networks have demonstrated high-quality reconstruction for single image super resolution (SISR). In this paper, we propose a Deep Residual Network via lateral modules (DRNLM), DRNLM is the structure with lateral modules, progressive and symmetric residual blocks (convolutional residual blocks and deconvolutional residual blocks). First, DRNLM introduces lateral modules, which are used to transmit low-level features (coarse residue) into high-level features (fine residue) effectively, thus finer residue can be obtained for better image reconstruction. Second, considering more channels can stack more details, progressive channels that vary from 64 to 256 are utilized in DRNLM through residual blocks. Third, symmetric residual blocks have same dimensions of input and output, which can ensure the gradient ranging within certain limits when the network goes deeper. Extensive experiments demonstrate that the proposed method outperforms the existing methods in accuracy and visual impression.