Realistic_Decision99 t1_j2p6qn0 wrote on January 2, 2023 at 11:59 PM

The lens distortion models that are used extensively are all linear. Maybe instead of using a GAN you could use a simpler type of network, like a fully connected dense one, to effectively fit an unknown non-linear model. This could reflect additive noise from other types of distortion (e.g. due to sensor topography), or complex lens distortions (combination of multiple distortion effects).

LordChips4 OP t1_j2pd8zf wrote on January 3, 2023 at 12:44 AM

The distortion I'm aiming to correct is not from the lens but from a qr code posted on a cylindrical surface ( e.g. qr code posted on lampost), with an unknown radius. So ( at least by my understanding) there's no parameters? So my input would be a distorted qr code image and the output from the trained network would be the predicted qr code image without distortion. Am I wrong in my approach/way of thinking? I copy pasted the response from above since I feel it fits here aswell!