Different camera models are interesting field of research. For example NASA uses CAHVORE model for scientific cameras to correct for all sorts of distortions, and I think OpenCV has also it's own models. And now we have this novel model of lens blurs that can be added to the mix.

This is all very relevant if you want to do e.g. 3d reconstruction or view synthesis from some images, I imagine you can do much better the better knowledge you have of the camera.

I always wondered why lens blur is considered hard computationally. Lens mathematics seems pretty well understood given that we can create quite complex lens designs with incredible performance (take a look at modern DSLR lenses, they often have 10+ elements). And in general blurs (e.g. Gaussian) are not complex algorithms. Are there situations where lens blurs are easier/harder? I heard for 2D images it's hard to add blur - seems true because most smartphone artificial bokeh is horrible despite significant effort there. Presumably because depth information is missing? Is it easier for raytraced 3D renders?

I can't imagine there being enough information for true fingerprinting of individual devices. With ten million iPhones being made per month, surely the blur patterns have to have some overlap?

I have always found it odd how in VFX we spend a lot of time degrading our perfect 3D renders: motion blur, film grain, sensor noise and lens blur, which I would call a defocus. I am interested in the application of this research, and imagine a library of typical cameras and their associated blurs. We have similar libraries of film grain and sensor noises.

This would seem to have huge forensic applications.

Does this lens blur change over time for a given phone?