Polynomial Distortion Model

Polynomial distortion models are mathematical representations used to correct geometric distortions in images and data, primarily focusing on radial distortions. Current research emphasizes optimizing these models for various applications, including improving watermarking techniques for large language models, enhancing image quality from fisheye cameras, and preserving data utility in privacy-preserving federated learning. These advancements leverage techniques like dual gradient ascent algorithms and deep convolutional neural networks to achieve near-optimal trade-offs between distortion correction and other critical factors such as detection accuracy or privacy preservation, impacting fields ranging from computer vision to machine learning.