Plug-and-Play video super-resolution using edge-preserving filtering

Abstract

Video super-resolution objective is based on high resolution video frame reconstruction after downsampling. Such technology overcomes the inherent resolution limitations in videos. The mentioned task considers the inverse problem of original high resolution video frame recovery using prior information and reasonable suppositions. Motivated by the notable results of learning-based super-resolution strategies in obtaining high resolution outcomes from low resolution inputs, we leverage student-t mixture model as a promising reconstruction tool regarding video super-resolution. Student-t mixture model has a heavy tail which makes it robust and suitable for video frame patch prior and a rich mixture model in terms of log likelihood for information retrieval. Furthermore, in order to overcome the potential data uncertainties, edge-preserving filtering is applied to detect and preserve the video frame areas where the light sharply changes. For this purpose, we consider video frames in the framework of patches which will be selected on each frame based on their high amount of information. Afterwards, we use Plug-and-Play structure in order to apply student-t mixture prior model along with edge preserving filtering for representing the video frame patch prior in super-resolution algorithm. Finally, the proposed algorithm is evaluated empirically over 5 video frame sets, escalator, fountain, tree, wave and traffic. In addition, the results are compared with eight other state of the art super-resolution methods including SAN, RCAN, RDN, ANR, A+, SRCNN, FSRCNN-s and SelfExSR and it is proved that the proposed framework generally provides the best results in comparison with other techniques over four different super-resolution scales.