Bayesian formulation of regularization by denoising - Model and Monte Carlo sampling
Abstract
Image restoration aims at recovering a clean image from degraded observations. This paper presents a novel Bayesian framework for image restoration using a regularization-bydenoising (RED) prior. It introduces a probabilistic counterpart to the RED paradigm, and proposes a new Monte Carlo algorithm to efficiently sample from the resulting posterior distribution. The proposed method benefit from the recent developments of deep learning-based denoisers. Extensive numerical experiments illustrate the efficiency of the proposed method, showcasing its competitive performance against state-of-the-art methods.
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