2019/08/28 by Xuefei Yan, Yan, Xuefei, David J. Brady +13
Computer Science · Engineering · #Advanced Data Compression Techniques #Blind Source Separation Techniques #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Sparse and Compressive Sensing Techniques #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1908.10903
openalex publication_date 2019/08/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.