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A Streaming Motion Magnification Core for Smart Image Sensors.

Cong ShiGang Luo
Published in: IEEE transactions on circuits and systems. II, Express briefs : a publication of the IEEE Circuits and Systems Society (2017)
This paper proposes a modified Eulerian Video Magnification (EVM) algorithm and a hardware implementation of a motion magnification core for smart image sensors. Compared to the original EVM algorithm, we perform the pixel-wise temporal bandpass filtering only once rather than multiple times on all scale layers, to reduce the memory and multiplier requirement for hardware implementation. A pixel stream processing architecture with pipelined blocks is proposed for the magnification core, enabling it to readily fit common image sensing components with streaming pixel output, while achieving higher performance with lower system cost. We implemented an FPGA-based prototype that is able to process up to 90M pixels per second and magnify subtle motion. The motion magnification results are comparable to the original algorithm running on PC.
Keyphrases
  • deep learning
  • machine learning
  • high speed
  • primary care
  • healthcare
  • quality improvement
  • low cost
  • neural network