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Optical flow is a method aimed at predicting the movement velocity of anypixel in the image and is used in medicine and biology to estimate flow ofparticles in organs or organelles. However, a precise optical flow measurementrequires images taken at high speed and low exposure time, which inducesphototoxicity due to the increase in illumination power. We are looking here toestimate the three-dimensional movement vector field of moving out-of-planeparticles using normal light conditions and a standard microscope camera.  We present a method to predict, from a single textured wide-field microscopyimage, the movement of out-of-plane particles using the local characteristicsof the motion blur. We estimated the velocity vector field from the localestimation of the blur model parameters using an deep neural network andachieved a prediction with a regression coefficient of 0.92 between the groundtruth simulated vector field and the output of the network. This method couldenable microscopists to gain insights about the dynamic properties of sampleswithout the need for high-speed cameras or high-intensity light exposure.

Estimating Nonplanar Flow from 2D Motion-blurred Widefield Microscopy Images via Deep Learning