Dense-Connected Residual Network for Video Super-Resolution

Recent research has shown that the performances of super-resolution methods can be significantly boosted using deep convolutional neural networks. However, current superresolution methods continue to exhibit relatively low performances for video, partly because they ignore certain crucial inter-frame information from the original low-resolution frame sequence or the hierarchical features of deep networks. In this paper, we propose a novel method for video super resolution named dense-connected residual network (DCRnet) to address the above drawbacks. The DCRnet can preserve the low -frequency contents of motion compensated frames, and facilitate the restoration of high-frequency details by exploiting the hierarchical features from all the convolutional layers. Specifically, we propose a dense-connected residual block (DCRB) as a basic component. The output of one DCRB is the compressed concatenation of all preceding DCRBs features and each residual block features of the current DCRB. Extensive experimentation demonstrates that our method is superior to the current state-of-the-art methods in both quantitative and qualitative metrics.