Ghosting Effect Removal for Multi-Frame Super-Resolution on CCTV Videos with Moving Objects

Abstract

With the increased use of closed-circuit television (CCTV) footage for security and surveillance purposes as well as for object or person recognition and efficiency monitoring, high-quality CCTV videos are necessary. In this paper, we propose Corgi Eye, a moving object removal + super-resolution framework for enhancing CCTV footages to remove ghosting artifacts caused by performing multi-frame super-resolution (MISR) on moving objects. Our method extends the framework of Eagle Eye, which is an existing MISR framework tailored for mobile devices. Our results demonstrate that the system can completely remove ghosting effects caused by moving objects while performing MISR on CCTV footage. Our proposed method demonstrates competitive performance when compared to Eagle Eye, achieving a 16% increase in terms of PSNR metric. Additionally, our method can produce clear images, on par with deep learning approaches such as ESPCN and SOF-VSR.