Noise in imaging systems rarely conforms to the simple IID additive white Gaussian noise model. This tutorial provides a concise overview of alternative noise models that can be adopted in microscopy and microtomography applications. We emphasize two noise models: signal-dependent variance models and stationary spatially correlated models. We explore how to deal with them through nonlocal patch-based collaborative denoising filters such as BM3D. We also discuss the role of these noise models and filters as a versatile regularization prior for solving inverse imaging problems under the plug-and-play framework.
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