Geometric Distortion

Geometric distortion, the misrepresentation of spatial information in data, is a significant challenge across diverse fields, impacting the accuracy and reliability of analyses. Current research focuses on developing methods to detect, correct, and mitigate these distortions, employing techniques like deep learning-based models (e.g., networks for image registration and distortion correction) and self-supervised learning frameworks for training with limited labeled data. Addressing geometric distortion is crucial for improving the accuracy and fairness of applications ranging from language models and image quality assessment to medical imaging and remote sensing, ensuring reliable and equitable results.