Self-supervised denoising has attracted widespread attention due to its ability to train without clean images. However, noise in real-world scenarios is often spatially correlated, which causes many self-supervised algorithms that assume pixel-wise independent noise to perform poorly. Recent works have attempted to break noise correlation with downsampling or neighborhood masking. However, denoising on downsampled subgraphs can lead to aliasing effects and loss of details due to a lower sampling rate. Furthermore, the neighborhood masking methods either come with high computational complexity or do not consider local spatial preservation during inference. Through the analysis of existing methods, we point out that the key to obtaining high-quality and texture-rich results in real-world self-supervised denoising tasks is to train at the original input resolution structure and use asymmetric operations during training and inference. Based on this, we propose Asymmetric Tunable Blind-Spot Network (AT-BSN), where the blind-spot size can be freely adjusted, thus better balancing noise correlation suppression and image local spatial destruction during training and inference. In addition, we regard the pre-trained AT-BSN as a meta-teacher network capable of generating various teacher networks by sampling different blind-spots. We propose a blind-spot based multi-teacher distillation strategy to distill a lightweight network, significantly improving performance. Experimental results on multiple datasets prove that our method achieves state-of-the-art, and is superior to other self-supervised algorithms in terms of computational overhead and visual effects.

翻译：自监督去噪因无需干净图像即可训练而受到广泛关注。然而，真实场景中的噪声通常具有空间相关性，导致许多假设像素级噪声独立的自监督算法性能不佳。近期研究尝试通过下采样或邻域掩码来打破噪声相关性。但下采样子图上的去噪会因采样率降低而产生混叠效应并丢失细节。此外，邻域掩码方法要么计算复杂度高，要么在推理时未考虑局部空间保持。通过分析现有方法，我们指出在真实场景自监督去噪任务中获得高质量且纹理丰富结果的关键在于：在原始输入分辨率结构下训练，并在训练与推理过程中采用非对称操作。基于此，我们提出**非对称可调盲点网络（AT-BSN）**，其盲点尺寸可自由调整，从而在训练与推理中更好地平衡噪声相关性抑制与图像局部空间破坏。此外，我们将预训练的AT-BSN视为元教师网络，通过采样不同盲点可生成多种教师网络。我们提出基于盲点的多教师蒸馏策略来蒸馏轻量级网络，显著提升了性能。在多个数据集上的实验结果表明，我们的方法实现了最先进的性能，并且在计算开销和视觉效果上优于其他自监督算法。