Indeed, to apply any form of "denoising" is to presuppose what the "true" underlying sample looks like. All of these strategies that people like to apply, whether they know it or not, directly impose a model onto the initial dataset.
Once measurements are digitized, the uncertainty within the dataset is baked in and cannot be removed. Without external information, the best estimation of the distribution from which the data are drawn may be found within the data themselves.
Once measurements are digitized, the uncertainty within the dataset is baked in and cannot be removed. Without external information, the best estimation of the distribution from which the data are drawn may be found within the data themselves.