Multichannel image identification and restoration using the expectation-maximization algorithm

Brian C. Tom*, Aggelos K Katsaggelos

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


Previous work has demonstrated the effectiveness of the expectation-maximization algorithm to restore noisy, degraded single-channel images and simultaneously identify its blur. In addition, a general framework for processing multi-channel images using single-channel techniques has also been developed. This paper combines and extends the two approaches so that simultaneous restoration and blur identification is possible for multi-channel images. However, care must be taken in estimating the blur and the cross-power spectra, which are complex quantities. With this point in mind, explicit equations for simultaneous identification and restoration of noisy, blurred multi-channel images are developed, where the images may have cross-channel degradations. Experimental results are shown which support this multi-channel approach, and are compared with multi-channel Wiener filter results. Independently restoring each channel is also analyzed and compared with multi-channel results.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
EditorsAndrew G. Tescher
PublisherSociety of Photo-Optical Instrumentation Engineers
Number of pages16
ISBN (Print)0819416223
StatePublished - Dec 1 1994
EventApplications of Digital Image Processing XVII - San Diego, CA, USA
Duration: Jul 26 1994Jul 29 1994


OtherApplications of Digital Image Processing XVII
CitySan Diego, CA, USA

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Condensed Matter Physics

Fingerprint Dive into the research topics of 'Multichannel image identification and restoration using the expectation-maximization algorithm'. Together they form a unique fingerprint.

Cite this