Blind super-resolution image reconstruction using a maximum a posteriori estimation

Yu He, Kim Hui Yap, Li Chen, Lap Pui Chau

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

22 Citations (Scopus)


This paper proposes a new algorithm to address blind image super-resolution by fusing multiple blurred low-resolution (LR) images to render a high-resolution (HR) image. Conventional super-resolution (SR) image reconstruction algorithms assume either the blurring during the image formation process is negligible or the blurring function is known a priori. This assumption, however, is impractical as it is difficult to eliminate blurring completely in some applications or characterize the blurring function fully. In view of this, we present a new maximum a posteriori (MAP) estimation framework that performs joint blur identification and HR image reconstruction. An iterative scheme based on alternating minimization is developed to estimate the blur and HR image progressively. A blur prior that incorporates the soft parametric blur information and smoothness constraint is introduced in the proposed method. Experimental results show that the new method is effective in performing blind SR image reconstruction where there is limited information about the blurring function.

Original languageEnglish
Title of host publication2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings
Number of pages4
Publication statusPublished - Oct 2006
Externally publishedYes
Event2006 IEEE International Conference on Image Processing, ICIP 2006 - Atlanta, GA, United States
Duration: 8 Oct 200611 Oct 2006

Publication series

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


Conference2006 IEEE International Conference on Image Processing, ICIP 2006
Country/TerritoryUnited States
CityAtlanta, GA


  • Conjugate gradient methods
  • Image resolution
  • Image restoration
  • MAP estimation

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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