แนวคิดหลัก
Exploring spatial adaptation and temporal coherence in diffusion models for effective video super-resolution.
บทคัดย่อ
This content discusses the challenges of utilizing diffusion models for video super-resolution, proposing a novel approach called SATeCo. The approach focuses on learning spatial-temporal guidance from low-resolution videos to enhance high-resolution video denoising and reconstruction. It introduces Spatial Feature Adaptation (SFA) and Temporal Feature Alignment (TFA) modules to regulate the diffusion process. Extensive experiments on datasets demonstrate the effectiveness of SATeCo in improving spatial quality and temporal consistency.
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Introduction
- Diffusion models have shown progress in image generation.
- Videos present additional challenges due to an extra time dimension.
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Diffusion Models for Super-Resolution
- Utilizing pre-trained diffusion models for image super-resolution.
- Challenges include stochasticity affecting visual appearance preservation.
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Proposed Approach: SATeCo
- Focuses on Spatial Adaptation and Temporal Coherence.
- Utilizes SFA and TFA modules to guide high-resolution video synthesis.
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Experimental Results
- Superior performance of SATeCo demonstrated on REDS4 and Vid4 datasets.
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Model Analysis
- Impact of SFA and TFA modules on overall performance.
- Effectiveness of the video upscaler and refiner components.
สถิติ
"Extensive experiments conducted on the REDS4 and Vid4 datasets demonstrate the effectiveness of our approach."
"The LR latent feature maps G = Ez(Z), which are further utilized to guide the HR feature learning in UNet decoder."
คำพูด
"No natural way is to utilize the pre-trained diffusion models for image super-resolution, e.g., StableSR [46], to magnify each video frame."
"The proposed SATeCo explores spatial adaptation and temporal coherence in diffusion models for video super-resolution."