  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.
