Enhancing Perceptual Quality in Video Super-Resolution through
  Temporally-Consistent Detail Synthesis using Diffusion Models
Claudio Rota Marco Buzzelli Joost van de Weijer
In this paper, we address the problem of video super-resolution (VSR) using
Diffusion Models (DM), and present StableVSR. Our method significantly enhances
the perceptual quality of upscaled videos by synthesizing realistic and
temporally-consistent details. We turn a pre-trained DM for single image
super-resolution into a VSR method by introducing the Temporal Conditioning
Module (TCM). TCM uses Temporal Texture Guidance, which provides
spatially-aligned and detail-rich texture information synthesized in adjacent
frames. This guides the generative process of the current frame toward
high-quality and temporally-consistent results. We introduce a Frame-wise
Bidirectional Sampling strategy to encourage the use of information from past
to future and vice-versa. This strategy improves the perceptual quality of the
results and the temporal consistency across frames. We demonstrate the
effectiveness of StableVSR in enhancing the perceptual quality of upscaled
videos compared to existing state-of-the-art methods for VSR. The code is
available at https://github.com/claudiom4sir/StableVSR.