Information-Driven Design of Imaging Systems
Researchers present a framework that estimates mutual information directly from noisy measurements using a known noise model and a learned model of measurement variation, enabling direct evaluation and optimization of imaging systems by information content. In their NeurIPS 2025 paper, information estimates predicted decoder performance across color photography, radio astronomy, lensless imaging, and microscopy, and their IDEAL method optimized encoder parameters via gradient ascent, matching end-to-end optimization on color filter design without requiring a decoder network. The authors suggest the approach could extend to other sensing domains modeled as deterministic encoding with known noise characteristics.