A Fast MAP Algorithm for High-Resolution Image Reconstruction with Multisensors

Michael K. Ng*, Andy M. Yip

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

44 Citations (Scopus)

Abstract

In many applications, it is required to reconstruct a high-resolution image from multiple, undersampled and shifted noisy images. Using the regularization techniques such as the classical Tikhonov regularization and maximum a posteriori (MAP) procedure, a high-resolution image reconstruction algorithm is developed. Because of the blurring process, the boundary values of the low-resolution image are not completely determined by the original image inside the scene. This paper addresses how to use (i) the Neumann boundary condition on the image, i.e., we assume that the scene immediately outside is a reflection of the original scene at the boundary, and (ii) the preconditioned conjugate gradient method with cosine transform preconditioners to solve linear systems arising from the high-resolution image reconstruction with multisensors. The usefulness of the algorithm is demonstrated through simulated examples.

Original languageEnglish
Pages (from-to)143-164
Number of pages22
JournalMultidimensional Systems and Signal Processing
Volume12
DOIs
Publication statusPublished - Apr 2001

Scopus Subject Areas

  • Software
  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Artificial Intelligence
  • Applied Mathematics

User-Defined Keywords

  • MAP
  • regularization
  • cosine transform
  • image reconstruction
  • structured matrices

Fingerprint

Dive into the research topics of 'A Fast MAP Algorithm for High-Resolution Image Reconstruction with Multisensors'. Together they form a unique fingerprint.

Cite this