End-to-end meta-imagers: Information-theoretic objectives and generalized focusing optima

Abstract

Metasurfaces and complex photonic components are increasingly co-designed with computational back-ends via end-to-end optimization, yet such optimizations are expensive and opaque -- obscuring the role of the optics and any fundamental performance limits. We develop two information-theoretic objectives, based on Shannon capacity and Fisher information, that isolate the photonic contribution to image formation. Both are closed-form, data-free functions of the transfer matrix, requiring no training data, and yield designs whose reconstruction quality matches end-to-end optimization. We prove that for both objectives, and for a broader family with a shared mathematical structure, the optimal transfer matrix is a permutation matrix: each source's emission is concentrated on a single, distinct detector, a condition we call generalized focusing. This holds regardless of source/detector geometry, as we demonstrate in settings where conventional imaging intuition offers no guidance, including a two-way imager, imaging through a random scattering medium, and Hermite--Gauss mode sorting. The root of this constraint is an "intensity bottleneck": nonnegative intensity measurements admit only Kronecker deltas as a complete orthonormal basis. We further show that this bottleneck, and the generalized-focusing optimum, persist for coherent and partially coherent sources -- the constraint is the detector array, not the source coherence.