Image denoising is the first step in many biomedical image analysis pipelines
and Deep Learning (DL) based methods are currently best performing. A new
category of DL methods such as Noise2Void or Noise2Self can be used fully
unsupervised, requiring nothing but the noisy data. However, this comes at the
price of reduced reconstruction quality. The recently proposed Probabilistic
Noise2Void (PN2V) improves results, but requires an additional noise model for
which calibration data needs to be acquired. Here, we present improvements to
PN2V that (i) replace histogram based noise models by parametric noise models,
and (ii) show how suitable noise models can be created even in the absence of
calibration data. This is a major step since it actually renders PN2V fully
unsupervised. We demonstrate that all proposed improvements are not only
academic but indeed relevant.

该论文介绍了一种基于深度学习的图像去噪方法，将 PN2V 方法转化为完全无监督的方法，并且展示了新的方法将直方图噪音模型替换为参数噪音模型，即使没有校准数据，也可以创建适合的噪音模型。

全自动无监督概率噪音消除

Fully Unsupervised Probabilistic Noise2Void

Today, Convolutional Neural Networks (CNNs) are the leading method for image
denoising. They are traditionally trained on pairs of images, which are often
hard to obtain for practical applications. This motivates self-supervised
training methods such as Noise2Void~(N2V) that operate on single noisy images.
Self-supervised methods are, unfortunately, not competitive with models trained
on image pairs. Here, we present 'Probabilistic Noise2Void' (PN2V), a method to
train CNNs to predict per-pixel intensity distributions. Combining these with a
suitable description of the noise, we obtain a complete probabilistic model for
the noisy observations and true signal in every pixel. We evaluate PN2V on
publicly available microscopy datasets, under a broad range of noise regimes,
and achieve competitive results with respect to supervised state-of-the-art
methods.

本研究提出了一种名为 Probabilistic Noise2Void 的卷积神经网络训练方法，可用于单一噪声图像的去噪。通过与噪声的合适描述相结合，可以得到每个像素的噪声观测和真实信号的完整概率模型。在各种噪声情况下评估了该方法，应用于公开的显微镜数据集，其表现和有监督的现有方法相当。