Recently, denoising methods based on supervised learning have exhibited
promising performance. However, their reliance on external datasets containing
noisy-clean image pairs restricts their applicability. To address this
limitation, researchers have focused on training denoising networks using
solely a set of noisy inputs. To improve the feasibility of denoising
procedures, in this study, we proposed a single-image self-supervised learning
method in which only the noisy input image is used for network training. Gated
convolution was used for feature extraction and no-reference image quality
assessment was used for guiding the training process. Moreover, the proposed
method sampled instances from the input image dataset using Bernoulli sampling
with a certain dropout rate for training. The corresponding result was produced
by averaging the generated predictions from various instances of the trained
network with dropouts. The experimental results indicated that the proposed
method achieved state-of-the-art denoising performance on both synthetic and
real-world datasets. This highlights the effectiveness and practicality of our
method as a potential solution for various noise removal tasks.

利用单一图像进行自监督学习的提出方法利用门控卷积进行特征提取及无参考图像质量评估引导训练过程，采用伯努利采样从输入图像数据集中选择样本进行训练，并通过平均来自训练网络具有中断的各个实例的生成预测来产生相应结果，实验结果表明所提出的方法在合成和真实世界数据集上实现了最先进的去噪性能，突显了该方法作为各种噪声去除任务的有效性和实用性。

Self2Self+: 自监督学习与图像质量评估损失的单图像降噪

Self2Self+: Single-Image Denoising with Self-Supervised Learning and  Image Quality Assessment Loss

The lack of large-scale noisy-clean image pairs restricts supervised
denoising methods' deployment in actual applications. While existing
unsupervised methods are able to learn image denoising without ground-truth
clean images, they either show poor performance or work under impractical
settings (e.g., paired noisy images). In this paper, we present a practical
unsupervised image denoising method to achieve state-of-the-art denoising
performance. Our method only requires single noisy images and a noise model,
which is easily accessible in practical raw image denoising. It performs two
steps iteratively: (1) Constructing a noisier-noisy dataset with random noise
from the noise model; (2) training a model on the noisier-noisy dataset and
using the trained model to refine noisy images to obtain the targets used in
the next round. We further approximate our full iterative method with a fast
algorithm for more efficient training while keeping its original high
performance. Experiments on real-world, synthetic, and correlated noise show
that our proposed unsupervised denoising approach has superior performances
over existing unsupervised methods and competitive performance with supervised
methods. In addition, we argue that existing denoising datasets are of low
quality and contain only a small number of scenes. To evaluate raw image
denoising performance in real-world applications, we build a high-quality raw
image dataset SenseNoise-500 that contains 500 real-life scenes. The dataset
can serve as a strong benchmark for better evaluating raw image denoising. Code
and dataset will be released at this https URL

该论文提出了一种实用的无监督图像去噪方法，其只需要单个有噪声的图象和噪声模型，通过迭代的方式训练模型来实现去噪，得到了目前最先进的去噪效果，并构建了高质量的原始图像数据集 SenseNoise-500，以作为更好评估原始图像去噪性能的强大基准。