A Comparative Study of MAP and LMMSE Estimators for Blind Inverse Problems

TL;DR

This paper compares MAP and LMMSE estimators for blind inverse problems, showing LMMSE's robustness and potential as an initialization method for MAP, especially under controlled conditions.

Contribution

It provides a systematic comparison of MAP and LMMSE estimators in blind deconvolution, highlighting LMMSE's stability and utility as a baseline and initialization tool.

Findings

LMMSE estimator is more stable and reliable than MAP in controlled blind deconvolution.

LMMSE can effectively initialize MAP, improving its performance and reducing parameter sensitivity.

MAP methods remain unstable and require extensive tuning even in idealized settings.

Abstract

Maximum-a-posteriori (MAP) approaches are an effective framework for inverse problems with known forward operators, particularly when combined with expressive priors and careful parameter selection. In blind settings, however, their use becomes significantly less stable due to the inherent non-convexity of the problem and the potential non-identifiability of the solutions. (Linear) minimum mean square error (MMSE) estimators provide a compelling alternative that can circumvent these limitations. In this work, we study synthetic two-dimensional blind deconvolution problems under fully controlled conditions, with complete prior knowledge of both the signal and kernel distributions. We compare tailored MAP algorithms with simple LMMSE estimators whose functional form is closely related to that of an optimal Tikhonov estimator. Our results show that, even in these highly controlled settings, MAP methods remain unstable and require extensive parameter tuning, whereas the LMMSE estimator yields a robust and reliable baseline. Moreover, we demonstrate empirically that the LMMSE solution can serve as an effective initialization for MAP approaches, improving their performance and reducing sensitivity to regularization parameters, thereby opening the door to future theoretical and practical developments.