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In video denoising, the adjacent frames often provide very usefulinformation, but accurate alignment is needed before such information can beharnassed. In this work, we present a multi-alignment network, which generatesmultiple flow proposals followed by attention-based averaging. It serves tomimic the non-local mechanism, suppressing noise by averaging multipleobservations. Our approach can be applied to various state-of-the-art modelsthat are based on flow estimation. Experiments on a large-scale video datasetdemonstrate that our method improves the denoising baseline model by 0.2dB, andfurther reduces the parameters by 47% with model distillation. Code isavailable at https://github.com/IndigoPurple/MANet.

MANet: Improving Video Denoising with a Multi-Alignment Network