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Unrolled Variational Bayesian Algorithm for Image Blind Deconvolution (2110.07202v1)

Published 14 Oct 2021 in cs.CV, cs.AI, and math.OC

Abstract: In this paper, we introduce a variational Bayesian algorithm (VBA) for image blind deconvolution. Our generic framework incorporates smoothness priors on the unknown blur/image and possible affine constraints (e.g., sum to one) on the blur kernel. One of our main contributions is the integration of VBA within a neural network paradigm, following an unrolling methodology. The proposed architecture is trained in a supervised fashion, which allows us to optimally set two key hyperparameters of the VBA model and lead to further improvements in terms of resulting visual quality. Various experiments involving grayscale/color images and diverse kernel shapes, are performed. The numerical examples illustrate the high performance of our approach when compared to state-of-the-art techniques based on optimization, Bayesian estimation, or deep learning.

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Authors (3)
  1. Yunshi Huang (6 papers)
  2. Emilie Chouzenoux (52 papers)
  3. Jean-Christophe Pesquet (67 papers)
Citations (12)

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