Compressive Sampling for Array Cameras Article Swipe

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Compressed sensing Computer science Sampling (signal processing) Artificial intelligence Focus (optics) Digital camera Computer vision Electronics Layer (electronics) Deep learning Power (physics) Compression (physics) Image sensor Computer hardware Electrical engineering Engineering Materials science Optics Quantum mechanics Composite material Filter (signal processing) Physics

Xuefei Yan , David J. Brady , Jianqiang Wang , Chao Huang , Zian Li , Songsong Yan , Ди Лю , Zhan Ma ·

YOU? · · 2019 · Open Access · · DOI: https://doi.org/10.48550/arxiv.1908.10903 · OA: W2970741887

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.