L1 prior majorization in Bayesian image restoration
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
IEEE Transactions on Image Processing
Blind and semi-blind deblurring of natural images
IEEE Transactions on Image Processing
Maximum a posteriori video super-resolution using a new multichannel image prior
IEEE Transactions on Image Processing
A fast algorithm for robust mixtures in the presence of measurement errors
IEEE Transactions on Neural Networks
IEEE Transactions on Image Processing
Adaptive langevin sampler for separation of t-distribution modelled astrophysical maps
IEEE Transactions on Image Processing
Variational Bayesian image super-resolution with GPU acceleration
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Bayesian combination of sparse and non-sparse priors in image super resolution
Digital Signal Processing
A nonlinear level set model for image deblurring and denoising
The Visual Computer: International Journal of Computer Graphics
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Image priors based on products have been recognized to offer many advantages because they allow simultaneous enforcement of multiple constraints. However, they are inconvenient for Bayesian inference because it is hard to find their normalization constant in closed form. In this paper, a new Bayesian algorithm is proposed for the image restoration problem that bypasses this difficulty. An image prior is defined by imposing Student-t densities on the outputs of local convolutional filters. A variational methodology, with a constrained expectation step, is used to infer the restored image. Numerical experiments are shown that compare this methodology to previous ones and demonstrate its advantages.