论文标题
AP-BSN：通过非对称PD和盲点网络为真实世界图像进行自我监管
AP-BSN: Self-Supervised Denoising for Real-World Images via Asymmetric PD and Blind-Spot Network
论文作者
论文摘要
盲点网络（BSN）及其变体在自我监督的denoisising方面取得了重大进步。然而，由于较少实用的假设（如像素独立噪声），它们仍然属于合成嘈杂的输入。因此，使用自我监管的BSN处理空间相关的现实世界噪声是一项挑战。最近，已经提出了像素剃须的下采样（PD）来消除现实世界噪声的空间相关性。但是，直接整合PD和BSN并不是很微不足道的，这阻止了现实世界图像上完全自我监督的DeNoising模型。我们提出了一个不对称的PD（AP）来解决此问题，该问题引入了训练和推理的不同PD步幅。我们系统地表明，提出的AP可以解决由特定PD步步率因素引起的固有权衡，并使BSN适用于实际情况。为此，我们开发了AP-BSN，这是一种现实世界中SRGB图像的最先进的自我监督的DeNoising方法。我们进一步提出了随机替换精炼，这显着改善了我们的AP-BSN的性能，而无需任何其他参数。广泛的研究表明，我们的方法优于其他自我监督，甚至是不成对的denoising方法，而无需使用任何其他知识，例如噪声水平，就基本的未知噪声而言。
Blind-spot network (BSN) and its variants have made significant advances in self-supervised denoising. Nevertheless, they are still bound to synthetic noisy inputs due to less practical assumptions like pixel-wise independent noise. Hence, it is challenging to deal with spatially correlated real-world noise using self-supervised BSN. Recently, pixel-shuffle downsampling (PD) has been proposed to remove the spatial correlation of real-world noise. However, it is not trivial to integrate PD and BSN directly, which prevents the fully self-supervised denoising model on real-world images. We propose an Asymmetric PD (AP) to address this issue, which introduces different PD stride factors for training and inference. We systematically demonstrate that the proposed AP can resolve inherent trade-offs caused by specific PD stride factors and make BSN applicable to practical scenarios. To this end, we develop AP-BSN, a state-of-the-art self-supervised denoising method for real-world sRGB images. We further propose random-replacing refinement, which significantly improves the performance of our AP-BSN without any additional parameters. Extensive studies demonstrate that our method outperforms the other self-supervised and even unpaired denoising methods by a large margin, without using any additional knowledge, e.g., noise level, regarding the underlying unknown noise.
