Partial Coherence for Object Recognition and Depth Sensing
Zichen Xie Ken Xingze Wang
We show a monotonic relationship between performances of various computer
vision tasks versus degrees of coherence of illumination. We simulate partially
coherent illumination using computational methods, propagate the lightwave to
form images, and subsequently employ a deep neural network to perform object
recognition and depth sensing tasks. In each controlled experiment, we discover
that, increased coherent length leads to improved image entropy, as well as
enhanced object recognition and depth sensing performance.