Blind-spot networks (BSN) have been prevalent network architectures in
self-supervised image denoising (SSID). Existing BSNs are mostly conducted with
convolution layers. Although transformers offer potential solutions to the
limitations of convolutions and have demonstrated success in various image
restoration tasks, their attention mechanisms may violate the blind-spot
requirement, thus restricting their applicability in SSID. In this paper, we
present a transformer-based blind-spot network (TBSN) by analyzing and
redesigning the transformer operators that meet the blind-spot requirement.
Specifically, TBSN follows the architectural principles of dilated BSNs, and
incorporates spatial as well as channel self-attention layers to enhance the
network capability. For spatial self-attention, an elaborate mask is applied to
the attention matrix to restrict its receptive field, thus mimicking the
dilated convolution. For channel self-attention, we observe that it may leak
the blind-spot information when the channel number is greater than spatial size
in the deep layers of multi-scale architectures. To eliminate this effect, we
divide the channel into several groups and perform channel attention
separately. Furthermore, we introduce a knowledge distillation strategy that
distills TBSN into smaller denoisers to improve computational efficiency while
maintaining performance. Extensive experiments on real-world image denoising
datasets show that TBSN largely extends the receptive field and exhibits
favorable performance against state-of-the-art SSID methods. The code and
pre-trained models will be publicly available at
this https URL

本文提出了基于 transformer 的盲点网络（TBSN），通过分析和重新设计满足盲点要求的 transformer 操作符，扩展了其感受野，实现了有利于自监督图像去噪（SSID）任务处理的效果，并引入知识蒸馏策略提高计算效率。

TBSN: 基于 Transformer 的自监督图像降噪的盲点网络

TBSN: Transformer-Based Blind-Spot Network for Self-Supervised Image  Denoising

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.

提出了 PUCA，一种新的 J - 不变 U-Net 架构，用于自监督降噪，通过利用 patch-unshuffle/shuffle 大幅扩展接受域同时保持 J - 不变性和采用扩张注意力块 (DABs) 来进行全局上下文融合，实验证明 PUCA 在自监督图像降噪方面表现出色，优于现有方法。

PUCA：升级的自监督图像去噪中的补丁重排和通道注意力

PUCA: Patch-Unshuffle and Channel Attention for Enhanced Self-Supervised  Image Denoising

There have been many image denoisers using deep neural networks, which
outperform conventional model-based methods by large margins. Recently,
self-supervised methods have attracted attention because constructing a large
real noise dataset for supervised training is an enormous burden. The most
representative self-supervised denoisers are based on blind-spot networks,
which exclude the receptive field's center pixel. However, excluding any input
pixel is abandoning some information, especially when the input pixel at the
corresponding output position is excluded. In addition, a standard blind-spot
network fails to reduce real camera noise due to the pixel-wise correlation of
noise, though it successfully removes independently distributed synthetic
noise. Hence, to realize a more practical denoiser, we propose a novel
self-supervised training framework that can remove real noise. For this, we
derive the theoretic upper bound of a supervised loss where the network is
guided by the downsampled blinded output. Also, we design a conditional
blind-spot network (C-BSN), which selectively controls the blindness of the
network to use the center pixel information. Furthermore, we exploit a random
subsampler to decorrelate noise spatially, making the C-BSN free of visual
artifacts that were often seen in downsample-based methods. Extensive
experiments show that the proposed C-BSN achieves state-of-the-art performance
on real-world datasets as a self-supervised denoiser and shows qualitatively
pleasing results without any post-processing or refinement.

提出了一种基于盲点网络（blind-spot networks）的有条件盲点网络（C-BSN），采用随机子采样器进行空间去相关降噪，并在实际数据集上实现了最先进的性能。