Rectification Network

Rectification networks are neural network architectures designed to correct distortions or inconsistencies in data, improving the performance of downstream tasks. Current research focuses on applications such as image compression, medical image segmentation (using models like SAM), and geometric correction of wide-angle or fisheye images, often employing GANs or other deep learning approaches. These advancements enhance the accuracy and reliability of various computer vision and image processing applications, impacting fields ranging from medical imaging to virtual reality. Furthermore, research explores improving the efficiency and interpretability of rectification networks through techniques like algebraic rectifiers and refined training methods.