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arXiv · 1802.01770

Scale-recurrent Network for Deep Image Deblurring

Xin Tao·Hongyun Gao·Yi Wang·Xiaoyong Shen·Jue Wang·Jiaya Jia
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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 in [25], 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.

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Xin Tao, Hongyun Gao, Yi Wang, Xiaoyong Shen, Jue Wang, Jiaya Jia. 2018-02-06. Scale-recurrent Network for Deep Image Deblurring. https://arxiv.org/abs/1802.01770

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