Information-Driven Design of Imaging Systems
Henry Pinkard, Leyla Kabuli, Eric Markley, Tiffany Chien, Jiantao Jiao, Laura Waller
In modern imaging systems that computationally process raw measurements before or instead of human viewing, information content matters more than visual appearance. However, developing information estimators that can handle the complexity of real-world measurements yet remain practical enough for widespread use has proven challenging. We introduce a data-driven approach for estimating mutual information between unknown objects and their noisy measurements. Our technique fits probabilistic models to measurements and their noise processes, quantifying information content without requiring ground truth data or making assumptions about object structure. We validate our approach across diverse applications—color photography, radio astronomy, lensless imaging, and microscopy—demonstrating that information estimates reliably predict system performance. Finally, we introduce Information-Driven Encoder Analysis Learning (IDEAL), which optimizes imaging systems to maximize information capture. Our work unlocks information theory as a powerful, practical tool for analyzing and designing imaging systems across a broad range of applications.