Iterative Filter Adaptive Network for Single Image Defocus Deblurring
- Title
- Iterative Filter Adaptive Network for Single Image Defocus Deblurring
- Authors
- JUNYONG LEE; HYEONGSEOK SON; JAESUNG RIM; SUNGHYUN CHO; SEUNGYONG LEE
- Date Issued
- 2021-06-19
- Publisher
- CVPR
- 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
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/108249
- Article Type
- Conference
- Citation
- Conference on Computer Vision and Pattern Recognition (CVPR 2021), page. 2034 - 2042, 2021-06-19
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