Deblurring Dynamic Scenes via Spatially Varying Recurrent Neural Networks

Wenqi Ren, Jiawei Zhang, Jinshan Pan, Sifei Liu, Jimmy Ren, Junping Du, Xiaochun Cao, Ming Hsuan Yang

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


Deblurring images captured in dynamic scenes is challenging as the motion blurs are spatially varying caused by camera shakes and object movements. In this paper, we propose a spatially varying neural network to deblur dynamic scenes. The proposed model is composed of three deep convolutional neural networks (CNNs) and a recurrent neural network (RNN). The RNN is used as a deconvolution operator on feature maps extracted from the input image by one of the CNNs. Another CNN is used to learn the spatially varying weights for the RNN. As a result, the RNN is spatial-aware and can implicitly model the deblurring process with spatially varying kernels. To better exploit properties of the spatially varying RNN, we develop both one-dimensional and two-dimensional RNNs for deblurring. The third component, based on a CNN, reconstructs the final deblurred feature maps into a restored image. In addition, the whole network is end-to-end trainable. Quantitative and qualitative evaluations on benchmark datasets demonstrate that the proposed method performs favorably against the state-of-the-art deblurring algorithms.

Original languageEnglish
JournalIEEE transactions on pattern analysis and machine intelligence
Publication statusAccepted/In press - 2021

Bibliographical note

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All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


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