video2x

It applies deep learning models (Real-ESRGAN, waifu2x, SRMD, and others) to each video frame to hallucinate plausible high-frequency detail that the original lower-resolution source doesn't contain, producing sharper, more detailed output than bicubic upscaling.

The tool supports both CPU and GPU processing, with GPU acceleration dramatically reducing the processing time required to upscale video footage frame by framea computationally intensive operation that can take hours or days for long videos on CPU hardware.

Video2X supports multiple upscaling algorithms optimized for different content types: photo-realistic footage, animation, and mixed content each respond differently to super-resolution models, and the tool allows selecting the algorithm best matched to the source material type.

Video enthusiasts restoring old low-resolution footage to modern display standards, anime fans upscaling older animation that was produced at SD or 720p resolution, archivists digitizing historical video content, and content creators working with legacy footage that needs to match modern high-resolution production standards use Video2X to improve video quality without access to the original higher-resolution source.

The open-source model means processing happens locally without submitting video content to cloud enhancement services, important for footage with privacy or intellectual property considerations.