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Recently, image super-resolution has been widely studied and achievedsignificant progress by leveraging the power of deep convolutional neuralnetworks. However, there has been limited advancement in video super-resolution(VSR) due to the complex temporal patterns in videos. In this paper, weinvestigate how to adapt state-of-the-art methods of image super-resolution forvideo super-resolution. The proposed adapting method is straightforward. Theinformation among successive frames is well exploited, while the overhead onthe original image super-resolution method is negligible. Furthermore, wepropose a learning-based method to ensemble the outputs from multiplesuper-resolution models. Our methods show superior performance and rank secondin the NTIRE2019 Video Super-Resolution Challenge Track 1.

Adapting Image Super-Resolution State-of-the-arts and Learning Multi-model Ensemble for Video Super-Resolution