Multichannel image identification and restoration using continuous spatial domain modeling

Umar A. Al-Suwailem*, James Keller

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

4 Scopus citations

Abstract

In this paper, a novel identification technique for multichannel image processing is presented. Using the maximum likelihood estimation (ML) approach, the image is represented as an autoregressive (AR) model and blur is described as a continuous spatial domain model. Such formulation overcomes some major limitations encountered in other ML methods. Moreover, cross-spectral and spatial components are incorporated in the multichannel modeling. It is shown that by incorporating those components, the overall performance is improved significantly. Also, experimental results show that blur extent can be optimally identified from noisy color images that are degraded by uniform linear motion or out-of-focus blurs.

Original languageEnglish
Pages466-469
Number of pages4
StatePublished - 1997

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Multichannel image identification and restoration using continuous spatial domain modeling'. Together they form a unique fingerprint.

Cite this