Perceptual Video Super Resolution with Enhanced Temporal Consistency

Understand

With the advent of perceptual loss functions, new possibilities in super-resolution have emerged, and we currently have models that successfully generate near-photorealistic high-resolution images from their low-resolution observations.

• Up to now, however, such approaches have been exclusively limited to single image super-resolution.
• The application of perceptual loss functions on video processing still entails several challenges, mostly related to the lack of temporal consistency of the generated images, i.e., flickering artifacts.
• In this work, we present a novel adversarial recurrent network for video upscaling that is able to produce realistic textures in a temporally consistent way.