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Unrolled Variational Bayesian Algorithm for Image Blind Deconvolution
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DOI：10.1109/TIP.2022.3224322.png)
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Abstract
En 中文
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 state-of-the-art techniques based on optimization, Bayesian estimation, or deep learning.
Keywords:
Variational Bayesian approach
Kullback-Leibler divergence
majorization-minimization
blind deconvolution
image restoration
neural network
unrolling
deep learning