Semantic and Temporal Integration in Latent Diffusion Space for High-Fidelity Video Super-Resolution
This addresses the problem of enhancing low-resolution videos with high fidelity and temporal coherence for applications in video processing, but it appears incremental as it builds on latent diffusion models.
The paper tackles the challenge of achieving high fidelity alignment with low-resolution input while maintaining temporal consistency in video super-resolution, and the result is that SeTe-VSR outperforms existing methods in detail recovery and perceptual quality.
Recent advancements in video super-resolution (VSR) models have demonstrated impressive results in enhancing low-resolution videos. However, due to limitations in adequately controlling the generation process, achieving high fidelity alignment with the low-resolution input while maintaining temporal consistency across frames remains a significant challenge. In this work, we propose Semantic and Temporal Guided Video Super-Resolution (SeTe-VSR), a novel approach that incorporates both semantic and temporal-spatio guidance in the latent diffusion space to address these challenges. By incorporating high-level semantic information and integrating spatial and temporal information, our approach achieves a seamless balance between recovering intricate details and ensuring temporal coherence. Our method not only preserves high-reality visual content but also significantly enhances fidelity. Extensive experiments demonstrate that SeTe-VSR outperforms existing methods in terms of detail recovery and perceptual quality, highlighting its effectiveness for complex video super-resolution tasks.