PUCA: Patch-Unshuffle and Channel Attention for Enhanced Self-Supervised
  Image Denoising
Hyemi Jang Junsung Park Dahuin Jung Jaihyun Lew Ho Bae Sungroh Yoon
Although supervised image denoising networks have shown remarkable
performance on synthesized noisy images, they often fail in practice due to the
difference between real and synthesized noise. Since clean-noisy image pairs
from the real world are extremely costly to gather, self-supervised learning,
which utilizes noisy input itself as a target, has been studied. To prevent a
self-supervised denoising model from learning identical mapping, each output
pixel should not be influenced by its corresponding input pixel; This
requirement is known as J-invariance. Blind-spot networks (BSNs) have been a
prevalent choice to ensure J-invariance in self-supervised image denoising.
However, constructing variations of BSNs by injecting additional operations
such as downsampling can expose blinded information, thereby violating
J-invariance. Consequently, convolutions designed specifically for BSNs have
been allowed only, limiting architectural flexibility. To overcome this
limitation, we propose PUCA, a novel J-invariant U-Net architecture, for
self-supervised denoising. PUCA leverages patch-unshuffle/shuffle to
dramatically expand receptive fields while maintaining J-invariance and dilated
attention blocks (DABs) for global context incorporation. Experimental results
demonstrate that PUCA achieves state-of-the-art performance, outperforming
existing methods in self-supervised image denoising.