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.
@article{arxiv.2108.13610,
title = {Iterative Filter Adaptive Network for Single Image Defocus Deblurring},
author = {Junyong Lee and Hyeongseok Son and Jaesung Rim and Sunghyun Cho and Seungyong Lee},
journal= {arXiv preprint arXiv:2108.13610},
year = {2022}
}