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.

翻译：深度神经网络已催生众多图像去噪器，其性能远超传统基于模型的方法。近年来，自监督方法备受关注，因为构建大规模真实噪声数据集用于监督训练是一项艰巨任务。最具代表性的自监督去噪器基于盲点网络，该网络排除了感受野的中心像素。然而，排除任何输入像素都意味着舍弃部分信息，尤其是当对应输出位置处的输入像素被排除时。此外，标准盲点网络虽能成功去除独立分布的合成噪声，但由于噪声的像素相关性，在降低真实相机噪声方面效果欠佳。因此，为开发更实用的去噪器，我们提出一种新颖的自监督训练框架，可有效去除真实噪声。为此，我们推导了监督损失的理论上界，其中网络由下采样盲化输出引导。同时，我们设计了条件盲点网络（C-BSN），该网络可选择性控制网络的盲化程度以利用中心像素信息。进一步，我们利用随机下采样器在空间上解相关噪声，使C-BSN避免了下采样方法中常见的视觉伪影。大量实验表明，所提C-BSN作为自监督去噪器在真实数据集上达到业界领先性能，且无需后处理或精修即可呈现令人满意的视觉质量。