Recurrent Self-Supervised Video Denoising with Denser Receptive Field
Zichun Wang Yulun Zhang Debing Zhang Ying Fu
Self-supervised video denoising has seen decent progress through the use of
blind spot networks. However, under their blind spot constraints, previous
self-supervised video denoising methods suffer from significant information
loss and texture destruction in either the whole reference frame or neighbor
frames, due to their inadequate consideration of the receptive field. Moreover,
the limited number of available neighbor frames in previous methods leads to
the discarding of distant temporal information. Nonetheless, simply adopting
existing recurrent frameworks does not work, since they easily break the
constraints on the receptive field imposed by self-supervision. In this paper,
we propose RDRF for self-supervised video denoising, which not only fully
exploits both the reference and neighbor frames with a denser receptive field,
but also better leverages the temporal information from both local and distant
neighbor features. First, towards a comprehensive utilization of information
from both reference and neighbor frames, RDRF realizes a denser receptive field
by taking more neighbor pixels along the spatial and temporal dimensions.
Second, it features a self-supervised recurrent video denoising framework,
which concurrently integrates distant and near-neighbor temporal features. This
enables long-term bidirectional information aggregation, while mitigating error
accumulation in the plain recurrent framework. Our method exhibits superior
performance on both synthetic and real video denoising datasets. Codes will be
available at https://github.com/Wang-XIaoDingdd/RDRF.