Video super-resolution based on automatic key-frame selection and feature-guided variational optical flow

Abstract

This paper proposes a new video super-resolution method based on feature-guided variational optical flow. The key-frames are automatically selected and super-resolved using a method based on sparse regression. To overcome the blocking artifacts and deal with the case of small structures with large displacement, an efficient method based on feature-guided variational optical flow is used to super-resolve the non-key-frames. Experimental results show that the proposed method outperforms the existing benchmark in terms of both subjective visual quality and objective peak signal-to-noise ratio (PSNR). The average PSNR improvement from the bi-cubic interpolation is 7.15dB for four datasets. Thanks to the flexibility of designed automatic key-frame selection and the validness of feature-guided variational optical flow, the proposed method is applicable to various practical video sequences.