Improved self-supervised denoising for Poisson-Gaussian noise.
arXiv research
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A new dataset for denoising real fluorescence microscopy images.
In this paper, a methodology is investigated for signal recovery in the presence of non-Gaussian noise. In contrast with regularized minimization approaches often adopted in the literature, in our algorithm the regularization parameter is reliably estimated from the observations. As the posterior density of the unknown…
L2R learns to denoise images without needing noise distribution knowledge.
Variational inference has experienced a recent surge in popularity owing to stochastic approaches, which have yielded practical tools for a wide range of model classes. A key benefit is that stochastic variational inference obviates the tedious process of deriving analytical expressions for closed-form variable updates…
Paper explores supervised learning methods to approximate ideal observer for joint signal detection and localization.