Designing Imaging Systems Using Information Theory

Two problems plagued previous information-theoretic approaches to imaging: treating systems as unconstrained channels (inaccurate estimates) or requiring explicit object models (limited generality). The Berkeley AI Research team solved both by estimating mutual information directly from measurements, exploiting the known noise models of imaging systems. They decompose mutual information into total measurement variation minus noise-only variation; the latter is computed analytically from noise physics, while the former is learned from data using probabilistic models (Gaussian process, full Gaussian, or PixelCNN). Validation across color photography, radio astronomy, lensless imaging, and microscopy showed that higher information estimates consistently predicted better performance on downstream reconstruction tasks. Building on this, they introduce Information-Driven Encoder Analysis Learning (IDEAL), which uses gradient ascent on information estimates to optimize imaging system parameters without a decoder network. In color filter design, IDEAL matched end-to-end optimization in both information and reconstruction quality, while requiring less memory and compute. The framework promises to extend beyond imaging to other sensing domains with known noise characteristics.