Hierarchical blur identification from severely out-of-focus images

  • Authors:

  • Jungsoo Lee;Yoonjong Yoo;Jeongho Shin;Joonki Paik

  • Affiliations:

  • Image Processing and Intelligent Systems Laboratory, Department of Image Engineering, Graduate School of Advanced Imaging Science, Multimedia, and Film, Chung-Ang University, Seoul, Korea;Image Processing and Intelligent Systems Laboratory, Department of Image Engineering, Graduate School of Advanced Imaging Science, Multimedia, and Film, Chung-Ang University, Seoul, Korea;Department of Web Information Engineering, Hankyong National University, Ansung-city, Korea;Image Processing and Intelligent Systems Laboratory, Department of Image Engineering, Graduate School of Advanced Imaging Science, Multimedia, and Film, Chung-Ang University, Seoul, Korea

  • Venue:
  • PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
  • Year:
  • 2006

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Abstract

This paper proposes a blur identification method from severely out-of-focus images. The proposed blur identification algorithm can be used in digital auto-focusing and image restoration. Since it is not easy to estimate a point spread function (PSF) from severely out-of-focus images, a hierarchical approach is applied in the proposed algorithm. For severe out of focus blur, the proposed algorithm uses an hierarchical approach for estimating and selecting feasible PSF from successive down sampled images. The down sampled images contain more useful edge information for PSF estimation. The feasible PSF selected, can then be reconstructed for original image resolution level by up sampling methods. In order to reconstruct the PSF accurately, a regularized PSF reconstruction algorithm is used. Finally, we can restore the severely blurred image with the reconstructed PSF. Experimental results show that reconstructed PSF by the proposed hierarchical algorithm can efficiently restore severely out-of-focus images.