RDLNET: Residual Dense Block based Lightweight Network for Video Super-Resolution

Kuan-Hsien Liu, Chih-Jung Wang, Tsung-Jung Liu, Wen-Ren Liu

Abstract: Deep learning has been widely used in video super-resolution (VSR). Most VSR methods focus on achieving better quality, and the design of deep neural networks is also becoming more complex. Since the input data of video super-resolution is already huge, if the neural network is too complex, it will cause a higher memory load, and a higher-end graphic card device is required. In order to reduce the cost of VSR system, in the paper, we propose a new method called RDLNET, which is a residual dense block based neural network with lightweights to deal with VSR. In the experiments, our proposed lightweight VSR method reduces 25% parameters and maintains almost the same PSNR compared with other state-of-the-art VSR methods. The source code of RDLNET is made available at GitHub https://github.com/nutcliu2507/RDLNET.