Name
Abstract | ||
|---|---|---|
Reducing the defocus blur that arises from the finite aperture size and short exposure time is an essential problem in computational photography. It is very challenging because the blur kernel is spatially varying and difficult to estimate by traditional methods. Due to its great breakthrough in low-level tasks, convolutional neural networks (CNNs) have been introduced to the defocus deblurring pr... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/JAS.2022.105563 | IEEE/CAA Journal of Automatica Sinica |
Keywords | DocType | Volume |
Blur kernel,convolutional neural networks (CNNs),defocus deblurring,dual-pixel (DP) data,meta-learning | Journal | 9 |
Issue | ISSN | Citations |
5 | 2329-9266 | 0 |
PageRank | References | Authors |
0.34 | 21 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pengwei Liang | 1 | 0 | 0.68 |
| Junjun Jiang | 2 | 1138 | 74.49 |
| Xianming Liu | 3 | 461 | 47.55 |
| Jiayi Ma | 4 | 1302 | 65.86 |