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Cascading Refinement Video Denoising with Uncertainty Adaptivity

arXiv 2408.02284 ↗First posted 5 Aug 2024cs.CVcited by 0

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Abstract (arXiv)

Accurate alignment is crucial for video denoising. However, estimating alignment in noisy environments is challenging. This paper introduces a cascading refinement video denoising method that can refine alignment and restore images simultaneously. Better alignment enables restoration of more detailed information in each frame. Furthermore, better image quality leads to better alignment. This method has achieved SOTA performance by a large margin on the CRVD dataset. Simultaneously, aiming to deal with multi-level noise, an uncertainty map was created after each iteration. Because of this, redundant computation on the easily restored videos was avoided. By applying this method, the entire computation was reduced by 25% on average.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 27 of its 45 references that are in the map. First posted on arXiv 5 Aug 2024.

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References · 27

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15 more
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