Sampling Mask

Sampling masks are crucial in various image processing tasks, primarily aiming to optimize data acquisition and reconstruction by selectively sampling data points. Current research focuses on learning optimal mask designs, often integrating these masks directly into deep learning architectures like Vision Transformers and autoencoders, or using them to guide generative models for data augmentation. This research is significant because efficient sampling masks can reduce data acquisition time and computational costs in applications such as medical imaging (MRI, CT), hyperspectral imaging, and image compression, ultimately improving the efficiency and accessibility of these technologies.