Unrolled Variational Bayesian Algorithm for Image Blind Deconvolution

• URL: http://arxiv.org/abs/2110.07202v1
• Date: Thu, 14 Oct 2021 07:55:26 GMT
• Title: Unrolled Variational Bayesian Algorithm for Image Blind Deconvolution
• Authors: Yunshi Huang and Emilie Chouzenoux and Jean-Christophe Pesquet
• Abstract summary: We introduce a variational Bayesian algorithm (VBA) for image blind deconvolution. One of our main contributions is the integration of VBA within a neural network paradigm.
• Score: 13.097469614950109
• License: http://creativecommons.org/licenses/by/4.0/
• 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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