  Optical flow is a method aimed at predicting the movement velocity of any
pixel in the image and is used in medicine and biology to estimate flow of
particles in organs or organelles. However, a precise optical flow measurement
requires images taken at high speed and low exposure time, which induces
phototoxicity due to the increase in illumination power. We are looking here to
estimate the three-dimensional movement vector field of moving out-of-plane
particles using normal light conditions and a standard microscope camera.
  We present a method to predict, from a single textured wide-field microscopy
image, the movement of out-of-plane particles using the local characteristics
of the motion blur. We estimated the velocity vector field from the local
estimation of the blur model parameters using an deep neural network and
achieved a prediction with a regression coefficient of 0.92 between the ground
truth simulated vector field and the output of the network. This method could
enable microscopists to gain insights about the dynamic properties of samples
without the need for high-speed cameras or high-intensity light exposure.
