A POCS-based constrained total least squares algorithm for image restoration

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

10 Scopus Citations
View graph of relations


  • Xiangchao Gan
  • Alan Wee-Chung Liew
  • Hong Yan

Related Research Unit(s)


Original languageEnglish
Pages (from-to)986-1003
Journal / PublicationJournal of Visual Communication and Image Representation
Issue number5
Publication statusPublished - Oct 2006


In image restoration, the region of support of the point spread function is often much smaller than the size of the observed degraded image and this property is utilized in many image deconvolution algorithms. For the constrained total least squares (CTLS)-based algorithm, it means that the solution of the CTLS algorithm should retain the block-circulant and sparse structure of the degradation matrix simultaneously. In real image restoration problems, the CTLS method often involves large-scale computation and is often solved using Mesarovic et al.'s algorithm. However, there is concern about whether their algorithm preserves the sparse structure of the degradation matrix. In this paper, we prove that by imposing an extra constraint, the sparse structure in their algorithm can be preserved. Then, we use the projection onto convex sets algorithm to find a solution to this extended formulation. Our experimental study indicates that the proposed method performs competitively, and often better, in terms of visual and objective evaluations. © 2006 Elsevier Inc. All rights reserved.

Research Area(s)

  • Constrained total least squares algorithm, Image restoration, Projection onto convex sets algorithm