SIAM Journal on Imaging Sciences, Volume 14, Issue 1, Page 156-177, January 2021.
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.

中文翻译：

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阵列摄像机的压缩采样

SIAM影像科学杂志，第14卷，第1期，第156-177页，2021年1月。
尽管高性能镜头和图像传感器的设计长期以来一直是相机开发，图像数据处理的尺寸，重量和功能的重点。组件目前是根本提高相机分辨率的主要障碍。在这里，我们证明了深度学习-辅助压缩采样可以将摄像头电子设备的工作功率降低20倍或更多。迄今为止，传统的压缩采样已主要应用于物理传感器层。我们在这里表明，借助深度学习算法，压缩采样在数字层压缩中提供了独特的电源管理优势。