The Bayesian SIAC filter

Abstract

We propose the Bayesian Smoothness-Increasing Accuracy-Conserving (SIAC) filter---a hierarchical Bayesian generalization of the existing deterministic SIAC filter. The SIAC filter is a powerful numerical tool for removing high-frequency noise from data or numerical solutions without degrading accuracy. However, current SIAC methodology is limited to (i) nodal/modal data (noisy direct function values/coefficients of a piecewise polynomial function approximation) and (ii) deterministic point estimates that do not account for uncertainty propagation of input data to the SIAC reconstruction. The proposed Bayesian SIAC filter overcomes these limitations by (i) supporting general (non-nodal) data models and (ii) enabling rigorous uncertainty quantification (UQ), thereby broadening the applicability of SIAC filtering. We also develop structure-exploiting algorithms for efficient maximum a posteriori (MAP) estimation and Markov chain Monte Carlo (MCMC) sampling, with a focus on linear data models with additive Gaussian noise. Computational experiments demonstrate the effectiveness of the Bayesian SIAC filter across several applications, including signal denoising, image deblurring, and post-processing of numerical solutions to hyperbolic conservation laws. The results show that the Bayesian approach produces point estimates with accuracy comparable to, and in some cases exceeding, that of the deterministic SIAC filter. In addition, it extends naturally to general data models and provides built-in UQ.