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

Learning to Estimate Kernel Scale and Orientation of Defocus Blur with Asymmetric Coded Aperture