Scale-Recurrent Network for Deep Image Deblurring

Xin Tao; Hongyun Gao; Xiaoyong Shen; Jue Wang; Jiaya Jia

doi:10.1109/cvpr.2018.00853

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. © 2018 IEEE.

Keywords

DeblurringComputer scienceArtificial intelligenceImage (mathematics)Pyramid (geometry)Image restorationTask (project management)Scale (ratio)Deep learningArtificial neural networkComputer visionRecurrent neural networkPattern recognition (psychology)Image processingMathematicsEngineeringGeography

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Publication Info

Year: 2018
Type: preprint
Pages: 8174-8182
Citations: 1256
Access: Closed

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APA Style

                            
                                    Xin Tao, 
                                
                                    Hongyun Gao, 
                                
                                    Xiaoyong Shen
                                
                                et al.
                            
                            (2018). 
                            Scale-Recurrent Network for Deep Image Deblurring. 
                            
                            , 8174-8182.
                            https://doi.org/10.1109/cvpr.2018.00853

Identifiers

DOI: 10.1109/cvpr.2018.00853