Real-time video super resolution network using recurrent multi-branch dilated convolutions
作者：
Highlights：
• A new multi-branch dilated module to effectively improve network receptive field.
• Extracting spatial-temporal features at different scales in parallel.
• Resulting in superior performance with minimal computational costs.
• A new recurrent architecture to process a consecutive multi-frame sequence.
• Extracting temporal and spatial features simultaneously for super-resolution.
• The proposed network can reconstruct high definition video clip up to 50 fps.
摘要
•A new multi-branch dilated module to effectively improve network receptive field.•Extracting spatial-temporal features at different scales in parallel.•Resulting in superior performance with minimal computational costs.•A new recurrent architecture to process a consecutive multi-frame sequence.•Extracting temporal and spatial features simultaneously for super-resolution.•The proposed network can reconstruct high definition video clip up to 50 fps.
论文关键词：
论文评审过程：Received 23 May 2020, Revised 30 November 2020, Accepted 25 January 2021, Available online 6 February 2021, Version of Record 11 February 2021.
论文官网地址：https://doi.org/10.1016/j.image.2021.116167
