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.
Original language | English (US) |
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Pages (from-to) | 156-177 |
Number of pages | 22 |
Journal | SIAM Journal on Imaging Sciences |
Volume | 14 |
Issue number | 1 |
DOIs | |
State | Published - 2021 |
Externally published | Yes |
Keywords
- compressive sampling
- gigapixel imaging
- neural compression
ASJC Scopus subject areas
- General Mathematics
- Applied Mathematics