Recent advances in deep learning have been pushing image denoising techniques
to a new level. In self-supervised image denoising, blind-spot network (BSN) is
one of the most common methods. However, most of the existing BSN algorithms
use a dot-based central mask, which is recognized as inefficient for images
with large-scale spatially correlated noise. In this paper, we give the
definition of large-noise and propose a multi-mask strategy using multiple
convolutional kernels masked in different shapes to further break the noise
spatial correlation. Furthermore, we propose a novel self-supervised image
denoising method that combines the multi-mask strategy with BSN (MM-BSN). We
show that different masks can cause significant performance differences, and
the proposed MM-BSN can efficiently fuse the features extracted by multi-masked
layers, while recovering the texture structures destroyed by multi-masking and
information transmission. Our MM-BSN can be used to address the problem of
large-noise denoising, which cannot be efficiently handled by other BSN
methods. Extensive experiments on public real-world datasets demonstrate that
the proposed MM-BSN achieves state-of-the-art performance among self-supervised
and even unpaired image denoising methods for sRGB images denoising, without
any labelling effort or prior knowledge. Code can be found in
this https URL

本文提出一种利用多种掩码策略和 BSN 相结合的多掩膜策略（MM-BSN）来解决大噪声去噪问题的自监督图像去噪方法，实验结果表明该方法在 sRGB 图像去噪方面表现出良好的性能并且是目前自监督和非配对图像去噪方法中表现最好的。

基于暗斑网络的多掩蔽实时自监督图像去噪技术

MM-BSN: Self-Supervised Image Denoising for Real-World with Multi-Mask  based on Blind-Spot Network

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.

提出了一个全新的 自监督盲点网络（BSN）方法， Asymmetric PD (BSN) 可以更好地应对现实中空间相关的噪声，并且不使用任何外部噪声参数就能在自监督的情况下实现对现实世界的真实图像进行去噪处理，达到了当前最优的去噪效果。

AP-BSN: 基于非对称 PD 和盲区网络的自监督去噪技术，适用于现实世界图像

AP-BSN: Self-Supervised Denoising for Real-World Images via Asymmetric  PD and Blind-Spot Network

We investigate the task of learning blind image denoising networks from an
unpaired set of clean and noisy images. Such problem setting generally is
practical and valuable considering that it is feasible to collect unpaired
noisy and clean images in most real-world applications. And we further assume
that the noise can be signal dependent but is spatially uncorrelated. In order
to facilitate unpaired learning of denoising network, this paper presents a
two-stage scheme by incorporating self-supervised learning and knowledge
distillation. For self-supervised learning, we suggest a dilated blind-spot
network (D-BSN) to learn denoising solely from real noisy images. Due to the
spatial independence of noise, we adopt a network by stacking 1x1 convolution
layers to estimate the noise level map for each image. Both the D-BSN and
image-specific noise model (CNN\_est) can be jointly trained via maximizing the
constrained log-likelihood. Given the output of D-BSN and estimated noise level
map, improved denoising performance can be further obtained based on the Bayes'
rule. As for knowledge distillation, we first apply the learned noise models to
clean images to synthesize a paired set of training images, and use the real
noisy images and the corresponding denoising results in the first stage to form
another paired set. Then, the ultimate denoising model can be distilled by
training an existing denoising network using these two paired sets. Experiments
show that our unpaired learning method performs favorably on both synthetic
noisy images and real-world noisy photographs in terms of quantitative and
qualitative evaluation.

本文提出了一个两阶段框架，通过结合自监督学习和知识蒸馏的方法来促进无配对洗涤网络的学习，该方法在合成和真实噪声图像的定量和定性评估方面表现良好。