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

Jisheng Li, Qi Dai, Jiangtao Wen

Abstract: Consistent in-focus input imagery is an essential precondition for machine vision systems to perceive the dynamic environment. A de-focus 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 ad-just 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.