Variational Dirichlet Blur Kernel Estimation
- Beihang Univ., Beijing (China); Northwestern University
- Univ. de Granada, Granada (Spain)
- Beihang Univ., Beijing (China)
- Northwestern Univ., Evanston, IL (United States)
Blind image deconvolution involves two key objectives: 1) latent image and 2) blur estimation. For latent image estimation, we propose a fast deconvolution algorithm, which uses an image prior of nondimensional Gaussianity measure to enforce sparsity and an undetermined boundary condition methodology to reduce boundary artifacts. For blur estimation, a linear inverse problem with normalization and nonnegative constraints must be solved. However, the normalization constraint is ignored in many blind image deblurring methods, mainly because it makes the problem less tractable. In this paper, we show that the normalization constraint can be very naturally incorporated into the estimation process by using a Dirichlet distribution to approximate the posterior distribution of the blur. Making use of variational Dirichlet approximation, we provide a blur posterior approximation that considers the uncertainty of the estimate and removes noise in the estimated kernel. Furthermore, experiments with synthetic and real data demonstrate that the proposed method is very competitive to the state-of-the-art blind image restoration methods.
- Research Organization:
- Northwestern Univ., Evanston, IL (United States). Center for Catalysis and Surface Science
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA), Office of Nonproliferation and Verification Research and Development (NA-22)
- Grant/Contract Number:
- NA0002520
- OSTI ID:
- 1487984
- Journal Information:
- IEEE Transactions on Image Processing, Journal Name: IEEE Transactions on Image Processing Journal Issue: 12 Vol. 24; ISSN 1057-7149
- Publisher:
- IEEECopyright Statement
- Country of Publication:
- United States
- Language:
- English
Similar Records
Fast Bayesian blind deconvolution with Huber Super Gaussian priors
MO-G-17A-05: PET Image Deblurring Using Adaptive Dictionary Learning