Fast Spatio-Temporal Residual Network for Video Super-Resolution

Understand

Recently, deep learning based video super-resolution (SR) methods have achieved promising performance.

• To simultaneously exploit the spatial and temporal information of videos, employing 3-dimensional (3D) convolutions is a natural approach.
• However, straight utilizing 3D convolutions may lead to an excessively high computational complexity which restricts the depth of video SR models and thus undermine the performance.
• In this paper, we present a novel fast spatio-temporal residual network (FSTRN) to adopt 3D convolutions for the video SR task in order to enhance the performance while maintaining a low computational load.