Simple iterative algorithm for image enhancement
ICAI'09 Proceedings of the 10th WSEAS international conference on Automation & information
Filtering vs. nonlinear estimation procedures for image enhancement
WSEAS Transactions on Computers
Bayesian blind deconvolution from differently exposed image pairs
IEEE Transactions on Image Processing
Variational method for super-resolution optical flow
Signal Processing
Surface Reconstruction and Image Enhancement via $L^1$-Minimization
SIAM Journal on Scientific Computing
Single-frame image recovery using a Pearson type VII MRF
Neurocomputing
Bayesian combination of sparse and non-sparse priors in image super resolution
Digital Signal Processing
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We present a novel method of Bayesian image super-resolution in which marginalization is carried out over latent parameters such as geometric and photometric registration and the image point-spread function. Related Bayesian super-resolution approaches marginalize over the high-resolution image, necessitating the use of an unfavourable image prior, whereas our method allows for more realistic image prior distributions, and reduces the dimension of the integral considerably, removing the main computational bottleneck of algorithms such as Tipping and Bishop's Bayesian image super-resolution. We show results on real and synthetic datasets to illustrate the efficacy of our method.