A VQ-based blind image restoration algorithm

Ryo Nakagaki*, Aggelos K. Katsaggelos

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

62 Scopus citations


In this paper, learning-based algorithms for image restoration and blind image restoration are proposed. Such algorithms deviate from the traditional approaches in this area, by utilizing priors that are learned from similar images. Original images and their degraded versions by the known degradation operator (restoration problem) are utilized for designing the VQ codebooks. The codevectors are designed using the blurred images. For each such vector, the high frequency information obtained from the original images is also available. During restoration, the high frequency information of a given degraded image is estimated from its low frequency information based on the codebooks. For the blind restoration problem, a number of codebooks are designed corresponding to various versions of the blurring function. Given a noisy and blurred image, one of the codebooks is chosen based on a similarity measure, therefore providing the identification of the blur. To make the restoration process computationally efficient, the Principal Component Analysis (PCA) and VQ-Nearest Neighborhood approaches are utilized. Simulation results are presented to demonstrate the effectiveness of the proposed algorithms.

Original languageEnglish (US)
Pages (from-to)1044-1053
Number of pages10
JournalIEEE Transactions on Image Processing
Issue number9
StatePublished - Sep 2003


  • Blur identification
  • Compression
  • Image restoration
  • Vector quantization

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


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