DeepDeblur: text image recovery from blur to sharp

Abstract

Digital images could be degraded by a variety of blur during the image acquisition (i.e. relative motion of cameras, electronic noise, capturing defocus, and so on). Blurring images can be computationally modeled as the result of a convolution process with the corresponding blur kernel and thus, image deblurring can be regarded as a deconvolution operation. In this paper, we explore to deblur images by approximating blind deconvolutions using a deep neural network. Different deep neural network structures are investigated to evaluate their deblurring capabilities, which contributes to the optimal design of a network architecture. It is found that shallow and narrow networks are not capable of handling complex motion blur. We thus, present a deep network with 20 layers to cope with text image blur. In addition, a novel network structure with Sequential Highway Connections (SHC) is leveraged to gain superior convergence. The experiment results demonstrate the state-of-the-art performance of the proposed framework with the higher visual quality of the delurred images.

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Correspondence to Xudong Jiang.

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Mei, J., Wu, Z., Chen, X. et al. DeepDeblur: text image recovery from blur to sharp. Multimed Tools Appl 78, 18869–18885 (2019). https://doi.org/10.1007/s11042-019-7251-y

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Keywords

  • Text Deblurring
  • Convolutional Neural Network (CNN)
  • Blind deconvolution
  • Short connection