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Unrolled Variational Bayesian Algorithm for Image Blind Deconvolution
- Source :
- IEEE Transactions on Image Processing, IEEE Transactions on Image Processing, 2022, 32, pp.430-445. ⟨10.1109/TIP.2022.3224322⟩
- Publication Year :
- 2022
- Publisher :
- HAL CCSD, 2022.
-
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.<br />Comment: 13 pages
- Subjects :
- FOS: Computer and information sciences
Kullback-Leibler divergence
Computer Science - Artificial Intelligence
neural network
Computer Vision and Pattern Recognition (cs.CV)
Computer Science - Computer Vision and Pattern Recognition
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
deep learning
Computer Graphics and Computer-Aided Design
image restoration
Majorization-Minimization
blind deconvolution
Artificial Intelligence (cs.AI)
unrolling
Optimization and Control (math.OC)
FOS: Mathematics
Mathematics - Optimization and Control
Variational Bayesian approach
[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Software
Subjects
Details
- Language :
- English
- ISSN :
- 10577149
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Image Processing, IEEE Transactions on Image Processing, 2022, 32, pp.430-445. ⟨10.1109/TIP.2022.3224322⟩
- Accession number :
- edsair.doi.dedup.....ab28c791ac032cc6fc56776d1d750c0c