A Structured-Surrogate Double-Model Approach to Regularization Parameter Selection

Abstract

Double-model methods select a regularization parameter by matching the discrepancy between two reconstructions of the same data to a prescribed target. The Structured-Surrogate Double-Model (SSDM) framework is developed around a computationally inexpensive structured surrogate of the forward operator and a spectrally explicit perturbation, with the selected parameter subsequently applied to the original inverse problem. Given a prescribed target, parameter selection requires neither prior knowledge of the noise level nor training data. For a class of Tikhonov regularization problems, the squared discrepancy is shown to attain a strict maximum at zero regularization, decay as $λ^{-2}$, and decrease strictly beyond an explicit phase-aware threshold. A complementary, data-dependent certificate is established to guarantee strict monotonicity on finite intervals, enabling unique and reliable target matching even when the global threshold is overly conservative. The discrepancy is further connected to directional roughness in image deblurring, providing a geometric interpretation of the selection principle. Experiments on synthetic images and on synthetically blurred natural photographs illustrate the theoretical findings and demonstrate the practical applicability of certified parameter selection.