Deep Learning for Handling Kernel/model Uncertainty in Image Deconvolution

Most existing non-blind image deconvolution methods assume that the given blurring kernel is error-free. In practice, blurring kernel often is estimated via some blind deblurring algorithm which is not exactly the truth. Also, the convolution model is only an approximation to practical blurring effect. It is known that non-blind deconvolution is susceptible to such a kernel/model error. Based on an error-in-variable (EIV) model of image blurring that takes kernel error into consideration, this paper presents a deep learning method for deconvolution, which unrolls a total-least-squares (TLS) estimator whose relating priors are learned by neural networks (NNs). The experiments showed that the proposed method is robust to kernel/model error. It noticeably outperformed existing solutions when deblurring images using noisy kernels, e.g. the ones estimated from existing blind motion deblurring methods.

• Publication year

  2020

• Venue

  Computer Vision and Pattern Recognition

• Publication date

  2020-06-01

• Fields of study

  Computer Science, Engineering

• Identifiers
  DOI 10.1109/CVPR42600.2020.00246
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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