Scale-Recurrent Network for Deep Image Deblurring

Xin Tao, Hongyun Gao, Xiaoyong Shen, Jue Wang, Jiaya Jia; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 8174-8182

Abstract


In single image deblurring, the ``coarse-to-fine'' scheme, i.e. gradually restoring the sharp image on different resolutions in a pyramid, is very successful in both traditional optimization-based methods and recent neural-network-based approaches. In this paper, we investigate this strategy and propose a Scale-recurrent Network (SRN-DeblurNet) for this deblurring task. Compared with the many recent learning-based approaches, it has a simpler network structure, a smaller number of parameters and is easier to train. We evaluate our method on large-scale deblurring datasets with complex motion. Results show that our method can produce better quality results than state-of-the-arts, both quantitatively and qualitatively.

Related Material


[pdf] [arXiv]
[bibtex]
@InProceedings{Tao_2018_CVPR,
author = {Tao, Xin and Gao, Hongyun and Shen, Xiaoyong and Wang, Jue and Jia, Jiaya},
title = {Scale-Recurrent Network for Deep Image Deblurring},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}