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This paper compares MAP and LMMSE estimators for blind deconvolution problems, focusing on scenarios with full knowledge of signal and kernel distributions. It finds that MAP estimators are unstable and require extensive tuning, even in controlled settings, while LMMSE provides a robust baseline. The study also demonstrates that LMMSE solutions can effectively initialize MAP methods, improving their performance and stability.
Even with perfect knowledge of signal and kernel distributions, MAP estimators struggle in blind deconvolution, highlighting the surprising robustness of LMMSE as a baseline and initializer.
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.