IFAN: Iterative Filter Adaptive Network for Single Image Defocus Deblurring

This replicate contains the demo of the following paper:

Iterative Filter Adaptive Network for Single Image Defocus Deblurring
Junyong Lee, Hyeongseok Son, Jaesung Rim, Sunghyun Cho, Seungyong Lee, CVPR 2021

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.

Citation

If you find this demo useful, please consider citing:

@InProceedings{Lee_2021_CVPR,
    author = {Lee, Junyong and Son, Hyeongseok and Rim, Jaesung and Cho, Sunghyun and Lee, Seungyong},
    title = {Iterative Filter Adaptive Network for Single Image Defocus Deblurring},
    booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    month = {June},
    year = {2021}
}