Single image deblurring with adaptive dictionary learning

Zhe Hu, Jia Bin Huang, Ming Hsuan Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

53 Citations (Scopus)

Abstract

We propose a motion deblurring algorithm that exploits sparsity constraints of image patches using one single frame. In our formulation, each image patch is encoded with sparse coefficients using an over-complete dictionary. The sparsity constraints facilitate recovering the latent image without solving an ill-posed deconvolution problem. In addition, the dictionary is learned and updated directly from one single frame without using additional images. The proposed method iteratively utilizes sparsity constraints to recover latent image, estimates the deblur kernel, and updates the dictionary directly from one single image. The final deblurred image is then recovered once the deblur kernel is estimated using our method. Experiments show that the proposed algorithm achieves favorable results against the state-of-the-art methods.

Original languageEnglish
Title of host publication2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings
Pages1169-1172
Number of pages4
DOIs
Publication statusPublished - 2010 Dec 1
Event2010 17th IEEE International Conference on Image Processing, ICIP 2010 - Hong Kong, Hong Kong
Duration: 2010 Sep 262010 Sep 29

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Other

Other2010 17th IEEE International Conference on Image Processing, ICIP 2010
CountryHong Kong
CityHong Kong
Period10/9/2610/9/29

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All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

Cite this

Hu, Z., Huang, J. B., & Yang, M. H. (2010). Single image deblurring with adaptive dictionary learning. In 2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings (pp. 1169-1172). [5651892] (Proceedings - International Conference on Image Processing, ICIP). https://doi.org/10.1109/ICIP.2010.5651892