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2025 | OriginalPaper | Chapter

Enhancing Perceptual Quality in Video Super-Resolution Through Temporally-Consistent Detail Synthesis Using Diffusion Models

Authors : Claudio Rota, Marco Buzzelli, Joost van de Weijer

Published in: Computer Vision – ECCV 2024

Publisher: Springer Nature Switzerland

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Abstract

In this paper, we address the problem of enhancing perceptual quality in video super-resolution (VSR) using Diffusion Models (DMs) while ensuring temporal consistency among frames. We present StableVSR, a VSR method based on DMs that can significantly enhance the perceptual quality of upscaled videos by synthesizing realistic and temporally-consistent details. We introduce the Temporal Conditioning Module (TCM) into a pre-trained DM for single image super-resolution to turn it into a VSR method. TCM uses the novel Temporal Texture Guidance, which provides it with 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. In addition, we introduce the novel 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 while achieving better temporal consistency compared to existing state-of-the-art methods for VSR. The project page is available at https://github.com/claudiom4sir/StableVSR.

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previous chapter Self-supervised Video Copy Localization with Regional Token Representation

next chapter RoGUENeRF: A Robust Geometry-Consistent Universal Enhancer for NeRF

Available only for authorised users

https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler.

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Title: Enhancing Perceptual Quality in Video Super-Resolution Through Temporally-Consistent Detail Synthesis Using Diffusion Models
Authors: Claudio Rota
Marco Buzzelli
Joost van de Weijer
Publisher: Springer Nature Switzerland
Book: Computer Vision – ECCV 2024
Print ISBN: 978-3-031-73253-9

Electronic ISBN: 978-3-031-73254-6

Copyright Year: 2025
DOI: https://doi.org/10.1007/978-3-031-73254-6_3

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