  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.
