Image Rectification

Image rectification aims to correct geometric distortions in images, improving their quality and enabling more accurate analysis. Current research focuses on developing efficient and robust rectification methods for various applications, employing techniques such as deep learning (e.g., GANs, convolutional neural networks), geometric transformations (e.g., projective transformations, isometric mappings), and novel algorithms tailored to specific distortion types (e.g., creases, fisheye lenses, radial distortion). These advancements are crucial for improving the performance of computer vision tasks across diverse domains, including document processing, medical image analysis, and object recognition, where distorted images can hinder accurate interpretation and analysis.