  We propose a novel end-to-end learning-based approach for single image
defocus deblurring. The proposed approach is equipped with a novel Iterative
Filter Adaptive Network (IFAN) that is specifically designed to handle
spatially-varying and large defocus blur. For adaptively handling
spatially-varying blur, IFAN predicts pixel-wise deblurring filters, which are
applied to defocused features of an input image to generate deblurred features.
For effectively managing large blur, IFAN models deblurring filters as stacks
of small-sized separable filters. Predicted separable deblurring filters are
applied to defocused features using a novel Iterative Adaptive Convolution
(IAC) layer. We also propose a training scheme based on defocus disparity
estimation and reblurring, which significantly boosts the deblurring quality.
We demonstrate that our method achieves state-of-the-art performance both
quantitatively and qualitatively on real-world images.
