Bayesian deblurring with integrated noise estimation
- 1 June 2011
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 2625-2632
- https://doi.org/10.1109/cvpr.2011.5995653
Abstract
Conventional non-blind image deblurring algorithms involve natural image priors and maximum a-posteriori (MAP) estimation. As a consequence of MAP estimation, separate pre-processing steps such as noise estimation and training of the regularization parameter are necessary to avoid user interaction. Moreover, MAP estimates involving standard natural image priors have been found lacking in terms of restoration performance. To address these issues we introduce an integrated Bayesian framework that unifies non-blind deblurring and noise estimation, thus freeing the user of tediously pre-determining a noise level. A sampling-based technique allows to integrate out the unknown noise level and to perform deblurring using the Bayesian minimum mean squared error estimate (MMSE), which requires no regularization parameter and yields higher performance than MAP estimates when combined with a learned high-order image prior. A quantitative evaluation demonstrates state-of-the-art results for both non-blind deblurring and noise estimation.Keywords
This publication has 21 references indexed in Scilit:
- Fields of ExpertsInternational Journal of Computer Vision, 2009
- Estimating Optimal Parameters for MRF Stereo from a Single Image PairIEEE Transactions on Pattern Analysis and Machine Intelligence, 2007
- Noise estimation for video processing based on spatio-temporal gradientsIEEE Signal Processing Letters, 2006
- Removing camera shake from a single photographPublished by Association for Computing Machinery (ACM) ,2006
- Training Methods for Image Noise Level Estimation on Wavelet ComponentsEURASIP Journal on Advances in Signal Processing, 2004
- Motion-based motion deblurringIeee Transactions On Pattern Analysis and Machine Intelligence, 2004
- Image Quality Assessment: From Error Visibility to Structural SimilarityIEEE Transactions on Image Processing, 2004
- Nonlinear image recovery with half-quadratic regularizationIEEE Transactions on Image Processing, 1995
- An iterative technique for the rectification of observed distributionsThe Astronomical Journal, 1974
- Bayesian-Based Iterative Method of Image Restoration*Journal of the Optical Society of America, 1972