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。