Iterative Filter Adaptive Network for Single Image Defocus Deblurring
- URL: http://arxiv.org/abs/2108.13610v1
- Date: Tue, 31 Aug 2021 04:27:07 GMT
- Title: Iterative Filter Adaptive Network for Single Image Defocus Deblurring
- Authors: Junyong Lee, Hyeongseok Son, Jaesung Rim, Sunghyun Cho, and Seungyong
Lee
- Abstract summary: 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 blur.
We demonstrate that our method achieves state-of-the-art performance both quantitatively and qualitatively on real-world images.
- Score: 14.631102120866283
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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