Despite the significant results on synthetic noise under simplified
assumptions, most self-supervised denoising methods fail under real noise due
to the strong spatial noise correlation, including the advanced self-supervised
blind-spot networks (BSNs). For recent methods targeting real-world denoising,
they either suffer from ignoring this spatial correlation, or are limited by
the destruction of fine textures for under-considering the correlation. In this
paper, we present a novel method called LG-BPN for self-supervised real-world
denoising, which takes the spatial correlation statistic into our network
design for local detail restoration, and also brings the long-range
dependencies modeling ability to previously CNN-based BSN methods. First, based
on the correlation statistic, we propose a densely-sampled patch-masked
convolution module. By taking more neighbor pixels with low noise correlation
into account, we enable a denser local receptive field, preserving more useful
information for enhanced fine structure recovery. Second, we propose a dilated
Transformer block to allow distant context exploitation in BSN. This global
perception addresses the intrinsic deficiency of BSN, whose receptive field is
constrained by the blind spot requirement, which can not be fully resolved by
the previous CNN-based BSNs. These two designs enable LG-BPN to fully exploit
both the detailed structure and the global interaction in a blind manner.
Extensive results on real-world datasets demonstrate the superior performance
of our method. this https URL

该论文提出了一种名为 LG-BPN 的新方法，通过在网络设计中考虑局部细节恢复的空间相关统计，并将全局依赖性建模能力引入之前基于 CNN 的 BSN 方法，能够全面利用盲方法中的详细结构和全局交互。

LG-BPN: 面向自监督实际降噪的局部和全局盲斑网络

LG-BPN: Local and Global Blind-Patch Network for Self-Supervised  Real-World Denoising

Long-range dependencies modeling, widely used in capturing spatiotemporal
correlation, has shown to be effective in CNN dominated computer vision tasks.
Yet neither stacks of convolutional operations to enlarge receptive fields nor
recent nonlocal modules is computationally efficient. In this paper, we present
a generic family of lightweight global descriptors for modeling the
interactions between positions across different dimensions (e.g., channels,
frames). This descriptor enables subsequent convolutions to access the
informative global features with negligible computational complexity and
parameters. Benchmark experiments show that the proposed method can complete
state-of-the-art long-range mechanisms with a significant reduction in extra
computing cost. Code available at
this https URL

本文提出一种轻量级全局描述符，以建模跨不同维度（如通道、帧）之间的位置交互作用，使得后续卷积能够合理地访问此全局特征，从而在计算成本和参数数量上有了巨大的降低，也完成了最有效的长距离机制。