cross-posted from: https://programming.dev/post/37278389
Optical blur is an inherent property of any lens system and is challenging to model in modern cameras because of their complex optical elements. To tackle this challenge, we introduce a high‑dimensional neural representation of blur—the lens blur field—and a practical method for acquisition.
The lens blur field is a multilayer perceptron (MLP) designed to (1) accurately capture variations of the lens 2‑D point spread function over image‑plane location, focus setting, and optionally depth; and (2) represent these variations parametrically as a single, sensor‑specific function. The representation models the combined effects of defocus, diffraction, aberration, and accounts for sensor features such as pixel color filters and pixel‑specific micro‑lenses.
We provide a first‑of‑its‑kind dataset of 5‑D blur fields—for smartphone cameras, camera bodies equipped with a variety of lenses, etc. Finally, we show that acquired 5‑D blur fields are expressive and accurate enough to reveal, for the first time, differences in optical behavior of smartphone devices of the same make and model.
Is it possible to use some kind of random noise algorithm to modify the image so that devices can't be uniquely identified like this anymore? Or would that not work?
The would have to be enough to obscure the lens' aberration, that would be an obnoxious amount of noise. Instead I think a better solution is to add micro distortion strategically to make identification ambiguous/inconclusive
perhaps simply putting something like cling wrap over the lens and moving it for each photo would be enough: adding some scratches and roughness that slightly changes each time you move it
Some parts of the image would not be changed enough, and like a partial fingerprint they could still be traced, the entire image has to be micro-distorted digitally to thoroughly jumble up the microblur image signature.
The sensor also has a unique "grain" structure and that has to be dispelled