  Consistent in-focus input imagery is an essential precondition for machine
vision systems to perceive the dynamic environment. A defocus blur severely
degrades the performance of vision systems. To tackle this problem, we propose
a deep-learning-based framework estimating the kernel scale and orientation of
the defocus blur to adjust lens focus rapidly. Our pipeline utilizes 3D ConvNet
for a variable number of input hypotheses to select the optimal slice from the
input stack. We use random shuffle and Gumbel-softmax to improve network
performance. We also propose to generate synthetic defocused images with
various asymmetric coded apertures to facilitate training. Experiments are
conducted to demonstrate the effectiveness of our framework.
