Image Acquisition

Image acquisition research focuses on improving the speed, efficiency, and robustness of capturing high-quality images across diverse applications. Current efforts center on developing novel algorithms, such as diffusion models and deep reinforcement learning, to optimize image acquisition parameters, reduce data annotation needs, and enhance image quality in challenging conditions (e.g., low light, motion blur). These advancements are crucial for various fields, including medical imaging, remote sensing, and robotics, enabling faster data collection, improved diagnostic accuracy, and more efficient automation of visual inspection tasks. The development of robust and efficient image acquisition methods is driving progress in numerous scientific and engineering disciplines.