OT question: I am merging (calculating the mean of) 16 short exposures of a nigh... | Hacker News

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		thyselius on May 28, 2017 \| parent \| context \| favorite \| on: How Bayesian Inference Works (2016) OT question: I am merging (calculating the mean of) 16 short exposures of a night photo with high ISO in order to remove noise and get wonderful night shots. Now I'm just averaging pixelvalue = (photo1.pixelvalue + photo2.pixelvalue) / numPhotos Is there a way to make this smarter with a bayesian approach? I'm thinking it couldmake a smarter guess what the actual pixelvalue should be rather than just the average. Any ideas would be appreciated!

balnaphone on May 29, 2017 | [–]

See https://scholar.google.com/scholar?hl=en&q=COMPARAMETRIC+IMA...

This paper discusses exactly the scenario you discuss.

Houshalter on May 28, 2017 | [–]

Median would probably be better because outlier pixels could distort the mean.

But how would you use a Bayesian approach? What exactly are you trying to predict? What are the inputs? What is the model?

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