MultiBoot Vsr: Multi-Stage Multi-Reference Bootstrapping for Video Super-Resolution

Ratheesh Kalarot, Fatih Porikli; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 0-0

To make the best use of the previous estimations and shared redundancy across the consecutive video frames, here we propose a scene and class agnostic, fully convolutional neural network model for 4xvideo super-resolution. One stage of our network is composed of a motion compensation based input subnetwork, a blending backbone, and a spatial upsampling subnetwork. We recurrently apply this network to reconstruct high-resolution frames and then reuse them as additional reference frames after reshuffling them into multiple low-resolution images. This allows us to bootstrap and enhance image quality progressively. Our experiments show that our method generates temporally consistent and high-quality results without artifacts. Our method is ranked as the second best based on the SSIM scores on the NTIRE2019 VSR Challenge, Clean Track.