High-speed image enhancement: Real-time super-resolution and artifact removal for degraded analog footage

In this work we tackle the challenge of enhancing the quality of analog recorded images in real-time. This involves two key aspects: super-resolution to improve visual detail, and artifact removal to address specific issues unique to analog footage. We propose ARENet, a memory-efficient architecture trained in an adversarial setting that can handle analog videos with VHS-like artifacts while maintaining small memory footprint compared to other approaches. The model improves on SRUnet (Vaccaro et al., 2021) by working on its shortcomings when it comes to the diverse spectrum of analog video borne artifacts. More over, in order to be able to process large archives of stored analog videos our model was purposefully designed for fast visual quality improvement (i.e. capable of operating faster than 25 FPS on consumer hardware) and small memory footprint. The experimental results show that the proposed single frame based method achieves better perceptual performances with respect to the compared models while maintaining real time capabilities and being more suited for unique analog video artifacts. Our proposed approach has immediate implications for various industrial applications that involve working with analog video footage, including broadcasting, film restoration, and historical document preservation. By enhancing the visual quality of these recordings in real-time, our method can improve viewer experience, facilitate more accurate analysis and interpretation of content, and enable the digitization and archiving of previously inaccessible or degraded materials. Code and samples are available at https://github.com/LoreBerli/VHSRestoration