Bayesian Inversion via Probabilistic Cellular Automata: an application to image denoising

TL;DR

This paper introduces a novel Bayesian inversion method using Probabilistic Cellular Automata (PCA) for image denoising, emphasizing its parallel efficiency and competitive performance compared to traditional Gibbs sampling.

Contribution

The paper presents a new PCA-based approach for Bayesian inverse problems, demonstrating its parallel implementation and effectiveness in image denoising tasks.

Findings

PCA method achieves higher PSNR and SSIM than Gibbs sampler.

PCA offers significant speedups in computation.

Numerical results validate PCA as a promising Bayesian inference tool.

Abstract

We propose using Probabilistic Cellular Automata (PCA) to address inverse problems with the Bayesian approach. In particular, we use PCA to sample from an approximation of the posterior distribution. The peculiar feature of PCA is their intrinsic parallel nature, which allows for a straightforward parallel implementation that allows the exploitation of parallel computing architecture in a natural and efficient manner. We compare the performance of the PCA method with the standard Gibbs sampler on an image denoising task in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM). The numerical results and the large speedups obtained with this approach suggest that PCA-based algorithms are a promising alternative for Bayesian inference in high-dimensional inverse problems.