  This paper proposes a novel deep learning approach for single image defocus
deblurring based on inverse kernels. In a defocused image, the blur shapes are
similar among pixels although the blur sizes can spatially vary. To utilize the
property with inverse kernels, we exploit the observation that when only the
size of a defocus blur changes while keeping the shape, the shape of the
corresponding inverse kernel remains the same and only the scale changes. Based
on the observation, we propose a kernel-sharing parallel atrous convolutional
(KPAC) block specifically designed by incorporating the property of inverse
kernels for single image defocus deblurring. To effectively simulate the
invariant shapes of inverse kernels with different scales, KPAC shares the same
convolutional weights among multiple atrous convolution layers. To efficiently
simulate the varying scales of inverse kernels, KPAC consists of only a few
atrous convolution layers with different dilations and learns per-pixel scale
attentions to aggregate the outputs of the layers. KPAC also utilizes the shape
attention to combine the outputs of multiple convolution filters in each atrous
convolution layer, to deal with defocus blur with a slightly varying shape. We
demonstrate that our approach achieves state-of-the-art performance with a much
smaller number of parameters than previous methods.
