RealisVSR: Detail-enhanced Diffusion for Real-World 4K Video Super-Resolution
Weisong Zhao, Jingkai Zhou, Xiangyu Zhu, Weihua Chen, Xiao-Yu Zhang, Zhen Lei, Fan Wang

TL;DR
RealisVSR introduces a novel diffusion-based approach with enhanced detail recovery and a new 4K benchmark, significantly improving real-world 4K video super-resolution performance.
Contribution
The paper presents a high-frequency detail-enhanced diffusion model with a new architecture, loss function, and 4K benchmark for improved real-world video super-resolution.
Findings
Outperforms existing methods on multiple benchmarks
Requires only 5-25% of training data of previous approaches
Excels in ultra-high-resolution video super-resolution
Abstract
Video Super-Resolution (VSR) has achieved significant progress through diffusion models, effectively addressing the over-smoothing issues inherent in GAN-based methods. Despite recent advances, three critical challenges persist in VSR community: 1) Inconsistent modeling of temporal dynamics in foundational models; 2) limited high-frequency detail recovery under complex real-world degradations; and 3) insufficient evaluation of detail enhancement and 4K super-resolution, as current methods primarily rely on 720P datasets with inadequate details. To address these challenges, we propose RealisVSR, a high-frequency detail-enhanced video diffusion model with three core innovations: 1) Consistency Preserved ControlNet (CPC) architecture integrated with the Wan2.1 video diffusion to model the smooth and complex motions and suppress artifacts; 2) High-Frequency Rectified Diffusion Loss…
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Taxonomy
TopicsAdvanced Image Processing Techniques · Image and Video Quality Assessment · Generative Adversarial Networks and Image Synthesis
