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

Ratheesh Kalarot, Fatih Porikli; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 0-0

Abstract


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.

Related Material


[pdf]
[bibtex]
@InProceedings{Kalarot_2019_CVPR_Workshops,
author = {Kalarot, Ratheesh and Porikli, Fatih},
title = {MultiBoot Vsr: Multi-Stage Multi-Reference Bootstrapping for Video Super-Resolution},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}