With sufficient paired training samples, the supervised deep learning methods have attracted much attention in image denoising because of their superior performance. However, it is still very challenging to widely utilize the supervised methods in real cases due to the lack of paired noisy-clean images. Meanwhile, most self-supervised denoising methods are ineffective as well when applied to the real-world denoising tasks because of their strict assumptions in applications. For example, as a typical method for self-supervised denoising, the original blind spot network (BSN) assumes that the noise is pixel-wise independent, which is much different from the real cases. To solve this problem, we propose a novel self-supervised real image denoising framework named Sampling Difference As Perturbation (SDAP) based on Random Sub-samples Generation (RSG) with a cyclic sample difference loss. Specifically, we dig deeper into the properties of BSN to make it more suitable for real noise. Surprisingly, we find that adding an appropriate perturbation to the training images can effectively improve the performance of BSN. Further, we propose that the sampling difference can be considered as perturbation to achieve better results. Finally we propose a new BSN framework in combination with our RSG strategy. The results show that it significantly outperforms other state-of-the-art self-supervised denoising methods on real-world datasets. The code is available at https://github.com/p1y2z3/SDAP.

翻译：具备充足成对训练样本时，基于监督学习的深度学习方法因其优越性能在图像去噪领域受到广泛关注。然而，由于缺乏成对的含噪-干净图像，监督方法在实际场景中仍难以广泛应用。与此同时，大多数自监督去噪方法因对应用场景存在严格假设，在真实世界去噪任务中同样效果不佳。例如，作为自监督去噪的典型方法，原始盲点网络假定噪声在像素层面独立分布，这与实际情况存在显著差异。为解决该问题，我们提出了一种名为"采样差异作为扰动"的新型自监督真实图像去噪框架，该框架基于随机子样本生成策略并采用循环样本差异损失函数。具体而言，我们深入探究了盲点网络的特性以使其更适配真实噪声。令人惊讶的是，我们发现对训练图像施加适当扰动可有效提升盲点网络的性能。进一步地，我们提出可将采样差异视为扰动以获得更优效果。最终，我们结合所提出的随机子样本生成策略构建了新型盲点网络框架。实验结果表明，该方法在真实世界数据集上显著优于其他最先进的自监督去噪方法。代码开源地址：https://github.com/p1y2z3/SDAP。