Abstract
In this paper, we consider Bayesian image denoising based on a Gaussian Markov random field (GMRF) model, for which we propose an new algorithm. Our method can solve Bayesian image denoising problems, including hyperparameter estimation, in O(n)-time, where n is the number of pixels in a given image. From the perspective of the order of the computational time, this is a state-of-the-art algorithm for the present problem setting. Moreover, the results of our numerical experiments we show our method is in fact effective in practice.
Original language | English |
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Pages (from-to) | 1629-1639 |
Number of pages | 11 |
Journal | IEICE Transactions on Information and Systems |
Volume | E101D |
Issue number | 6 |
DOIs | |
Publication status | Published - 2018 Jun |
Externally published | Yes |
Keywords
- Bayesian image denoising
- EM algorithm
- Gaussian Markov random field
- Linear-time algorithm
- Mean-field method
ASJC Scopus subject areas
- Software
- Hardware and Architecture
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering
- Artificial Intelligence