Compressive Sampling for Array Cameras

TL;DR

This paper introduces Deep-Learning-Aided Compressive Sampling (DLACS), a method that significantly reduces power consumption in camera electronics by leveraging deep learning to enhance digital layer compression, enabling higher resolution imaging with less power.

Contribution

The paper demonstrates that integrating deep learning with compressive sampling in digital processing can drastically reduce power consumption in camera systems, a novel approach compared to traditional physical sensor methods.

Findings

DLACS reduces camera electronics power by 20x.

Deep learning enhances compressive sampling effectiveness.

Digital layer compression offers power management benefits.

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 is currently the primary barrier to radical improvements in camera resolution. Here we show that Deep-Learning- Aided Compressive Sampling (DLACS) can reduce operating power on camera-head electronics by 20x. Traditional compressive sampling has to date been primarily applied in the physical sensor layer, we show here that with aid from deep learning algorithms, compressive sampling offers unique power management advantages in digital layer compression.