Abstract
The Gaussian mixture is a patch prior that has enjoyed tremendous success in image processing. In this work, by using Gaussian factor modeling, its dedicated expectation maximization (EM) inference, and a statistical filter selection and algorithm stopping rule, we develop SURE (Stein's unbiased risk estimator) guided piecewise linear estimation (S-PLE), a patch-based prior learning algorithm capable of delivering state-of-the-art performance at image denoising. In light of this algorithm's features and its results, we also seek to address the number of components to be included when setting up a Gaussian mixture for image patch modeling. By juxtaposing both options, we show that a simple learned prior can perform as well as, if not better than, a much richer yet fixed prior. © 2013 Society for Industrial and Applied Mathematics.
| Original language | English |
|---|---|
| Pages (from-to) | 999-1034 |
| Journal | SIAM Journal on Imaging Sciences |
| Volume | 6 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2013 |
| Externally published | Yes |
Bibliographical note
Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].Research Keywords
- EM algorithm
- Gaussian factor mixture
- Image denoising
- SURE
- Tensor structure