We consider the challenging blind denoising problem for Poisson-Gaussian
noise, in which no additional information about clean images or noise level
parameters is available. Particularly, when only "single" noisy images are
available for training a denoiser, the denoising performance of existing
methods was not satisfactory. Recently, the blind pixelwise affine image
denoiser (BP-AIDE) was proposed and significantly improved the performance in
the above setting, to the extent that it is competitive with denoisers which
utilized additional information. However, BP-AIDE seriously suffered from slow
inference time due to the inefficiency of noise level estimation procedure and
that of the blind-spot network (BSN) architecture it used. To that end, we
propose Fast Blind Image Denoiser (FBI-Denoiser) for Poisson-Gaussian noise,
which consists of two neural network models; 1) PGE-Net that estimates
Poisson-Gaussian noise parameters 2000 times faster than the conventional
methods and 2) FBI-Net that realizes a much more efficient BSN for pixelwise
affine denoiser in terms of the number of parameters and inference speed.
Consequently, we show that our FBI-Denoiser blindly trained solely based on
single noisy images can achieve the state-of-the-art performance on several
real-world noisy image benchmark datasets with much faster inference time (x
10), compared to BP-AIDE. The official code of our method is available at
this https URL

论文提出了一种针对 Poisson-Gaussian 噪声的快速均值盲去噪（Fast Blind Image Denoiser，FBI-Denoiser），该算法使用两个神经网络模型（PGE-Net 和 FBI-Net），其中 PGE-Net 可以比传统方法快 2000 倍地估计 Poisson-Gaussian 噪声参数，而 FBI-Net 可以实现更高效的像素逐点仿射去噪器的盲点网络（BSN），并在多个实际噪声图像基准数据集上获得了最先进的性能和更快的推理速度（10 倍于 BP-AIDE）。

FBI-Denoiser: 基于 Poisson-Gaussian 噪声的快速盲图像去噪器

FBI-Denoiser: Fast Blind Image Denoiser for Poisson-Gaussian Noise

We extend the blindspot model for self-supervised denoising to handle
Poisson-Gaussian noise and introduce an improved training scheme that avoids
hyperparameters and adapts the denoiser to the test data. Self-supervised
models for denoising learn to denoise from only noisy data and do not require
corresponding clean images, which are difficult or impossible to acquire in
some application areas of interest such as low-light microscopy. We introduce a
new training strategy to handle Poisson-Gaussian noise which is the standard
noise model for microscope images. Our new strategy eliminates hyperparameters
from the loss function, which is important in a self-supervised regime where no
ground truth data is available to guide hyperparameter tuning. We show how our
denoiser can be adapted to the test data to improve performance. Our
evaluations on microscope image denoising benchmarks validate our approach.

本文提出了一种改进的自监督去噪模型，该模型可以处理 Poisson-Gaussian 噪声，并且避免使用超参数，在低光显微镜等某些应用领域中取得了较好的性能表现，评估结果表明该模型优于其他基准模型。

自监督泊松 - 高斯去噪

Self-Supervised Poisson-Gaussian Denoising

Fluorescence microscopy has enabled a dramatic development in modern biology.
Due to its inherently weak signal, fluorescence microscopy is not only much
noisier than photography, but also presented with Poisson-Gaussian noise where
Poisson noise, or shot noise, is the dominating noise source. To get clean
fluorescence microscopy images, it is highly desirable to have effective
denoising algorithms and datasets that are specifically designed to denoise
fluorescence microscopy images. While such algorithms exist, no such datasets
are available. In this paper, we fill this gap by constructing a dataset - the
Fluorescence Microscopy Denoising (FMD) dataset - that is dedicated to
Poisson-Gaussian denoising. The dataset consists of 12,000 real fluorescence
microscopy images obtained with commercial confocal, two-photon, and wide-field
microscopes and representative biological samples such as cells, zebrafish, and
mouse brain tissues. We use image averaging to effectively obtain ground truth
images and 60,000 noisy images with different noise levels. We use this dataset
to benchmark 10 representative denoising algorithms and find that deep learning
methods have the best performance. To our knowledge, this is the first real
microscopy image dataset for Poisson-Gaussian denoising purposes and it could
be an important tool for high-quality, real-time denoising applications in
biomedical research.

本研究构建了 Fluorescence Microscopy Denoising (FMD) 数据集，并采用该数据集对 10 种代表性降噪算法进行了基准测试，结果表明深度学习方法具有最佳性能。该数据集是用于泊松高斯噪声去噪目的下的第一个真实显微图像数据集，可成为生物医学研究中高质量、实时去噪应用的重要工具。