Motion Compensation

Motion compensation aims to correct for movement artifacts in various imaging and sensing modalities, improving the accuracy and reliability of data analysis. Current research focuses on developing robust and efficient algorithms, often employing deep neural networks (including transformers and convolutional architectures) or optimization-based approaches with spline models, to handle diverse motion patterns and imaging characteristics. These advancements are crucial for applications ranging from medical imaging (e.g., fetal imaging, 4DCT) and video compression to robotics (e.g., prosthetic control, laser surgery) and 3D scanning, where accurate motion correction is essential for reliable results. The development of computationally efficient and adaptable methods remains a key focus.