  Self-supervised denoising has attracted widespread attention due to its
ability to train without clean images. However, noise in real-world scenarios
is often spatially correlated, which causes many self-supervised algorithms
that assume pixel-wise independent noise to perform poorly. Recent works have
attempted to break noise correlation with downsampling or neighborhood masking.
However, denoising on downsampled subgraphs can lead to aliasing effects and
loss of details due to a lower sampling rate. Furthermore, the neighborhood
masking methods either come with high computational complexity or do not
consider local spatial preservation during inference. Through the analysis of
existing methods, we point out that the key to obtaining high-quality and
texture-rich results in real-world self-supervised denoising tasks is to train
at the original input resolution structure and use asymmetric operations during
training and inference. Based on this, we propose Asymmetric Tunable Blind-Spot
Network (AT-BSN), where the blind-spot size can be freely adjusted, thus better
balancing noise correlation suppression and image local spatial destruction
during training and inference. In addition, we regard the pre-trained AT-BSN as
a meta-teacher network capable of generating various teacher networks by
sampling different blind-spots. We propose a blind-spot based multi-teacher
distillation strategy to distill a lightweight network, significantly improving
performance. Experimental results on multiple datasets prove that our method
achieves state-of-the-art, and is superior to other self-supervised algorithms
in terms of computational overhead and visual effects.
