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.

翻译：尽管有监督图像去噪网络在合成噪声图像上表现出色，但由于真实噪声与合成噪声之间的差异，这类网络在实际应用中常会失效。由于从真实世界采集干净-噪声图像对的成本极高，自监督学习（利用含噪输入本身作为目标）成为研究热点。为防止自监督去噪模型学习恒等映射，每个输出像素不应受其对应输入像素的影响——该约束被称为J-不变性。盲点网络（BSNs）一直是自监督图像去噪中确保J-不变性的主流选择。然而，通过引入下采样等额外操作构造BSN变体时，可能暴露盲化信息从而破坏J-不变性。因此，仅允许使用专为BSN设计的卷积，这限制了网络架构的灵活性。为突破这一限制，我们提出PUCA——一种新型满足J-不变性的U-Net架构用于自监督去噪。PUCA利用分块逆混洗/混洗操作，在保持J-不变性的同时显著扩大感受野，并采用扩张注意力模块（DABs）融合全局上下文信息。实验结果表明，PUCA在自监督图像去噪任务中取得了最优性能，全面超越现有方法。