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Journal Articles IEEE Transactions on Image Processing Year : 2022

Unrolled Variational Bayesian Algorithm for Image Blind Deconvolution


In this paper, we introduce a variational Bayesian algorithm (VBA) for image blind deconvolution. Our VBA generic framework incorporates smoothness priors on the unknown blur/image and possible affine constraints (e.g., sum to one) on the blur kernel, integrating the 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 leads 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 stateof-the-art techniques based on optimization, Bayesian estimation, or deep learning.
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Dates and versions

hal-03881393 , version 1 (01-12-2022)



Yunshi Huang, Emilie Chouzenoux, Jean-Christophe Pesquet. Unrolled Variational Bayesian Algorithm for Image Blind Deconvolution. IEEE Transactions on Image Processing, 2022, 32, pp.430-445. ⟨10.1109/TIP.2022.3224322⟩. ⟨hal-03881393⟩
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