Name
Title | ||
|---|---|---|
Image Patch Prior Learning Based on Random Neighborhood Resampling for Image Denoising |
Abstract | ||
|---|---|---|
Image patch priors become a popular tool for image denoising. The Gaussian mixture model (GMM) is remarkably effective in modelling natural image patches. However, GMM prior learning using the expectation maximisation (EM) algorithm is sensitive to the initialisation, often leading to low convergence rate of parameter estimation. In this study, a novel sampling method called random neighbourhood r... |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1049/iet-ipr.2018.5403 | IET Image Processing |
Keywords | DocType | Volume |
expectation-maximisation algorithm,Gaussian processes,image denoising,learning (artificial intelligence),mixture models | Journal | 14 |
Issue | ISSN | Citations |
5 | 1751-9659 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jian Ji | 1 | 20 | 4.03 |
| Jiajie Wei | 2 | 0 | 0.34 |
| Fan Guoliang | 3 | 0 | 0.34 |
| Bai Mengqi | 4 | 0 | 0.34 |
| Huang Jingjing | 5 | 0 | 0.34 |
| Qiguang Miao | 6 | 355 | 49.69 |