Name
Abstract | ||
|---|---|---|
While design of high-performance lenses and image sensors has long been the focus of camera development, the size, weight, and power of image data processing components are currently the primary barriers to radical improvements in camera resolution. Here we show that deep learning{aided compressive sampling can reduce operating power on camera head electronics by 20 times or more. Traditional compressive sampling has to date been primarily applied in the physical sensor layer. We show here that with the aid of deep learning algorithms, compressive sampling is offers unique power management advantages in digital layer compression. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1137/19M1283914 | SIAM JOURNAL ON IMAGING SCIENCES |
Keywords | DocType | Volume |
compressive sampling, gigapixel imaging, neural compression | Journal | 14 |
Issue | ISSN | Citations |
1 | 1936-4954 | 0 |
PageRank | References | Authors |
0.34 | 0 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xuefei Yan | 1 | 0 | 0.34 |
| David J. Brady | 2 | 338 | 21.97 |
| Weiping Zhang | 3 | 0 | 0.34 |
| Changzhi Yu | 4 | 0 | 0.34 |
| Yulin Jiang | 5 | 0 | 0.34 |
| Jianqiang Wang | 6 | 7 | 0.97 |
| Chao Huang | 7 | 7 | 2.15 |
| Zian Li | 8 | 0 | 0.34 |
| Zhan Ma | 9 | 576 | 45.61 |