Adaptive Subsampling

Adaptive subsampling techniques aim to optimize data acquisition by selectively sampling only the most informative data points, thereby accelerating data processing and reducing computational costs. Current research focuses on developing data-driven methods, often employing convolutional neural networks or integrating object detection algorithms, to generate case-specific or object-adaptive sampling patterns. These advancements are significantly impacting various fields, including medical imaging (MRI, specifically), deep learning training, and automotive radar, by enabling faster processing, improved energy efficiency, and enhanced reconstruction quality at reduced sampling rates. The resulting improvements in speed and efficiency are particularly valuable for resource-constrained applications and real-time processing.