Globally optimal smoothing functional for multichannel image restoration

Moon G. Kang*, Aggelos K Katsaggelos

*Corresponding author for this work

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

3 Scopus citations


It is expected that a globally optimal restored multichannel image should be superior to a suboptimally restored image without the use of cross-channel information. In this paper, a regularized multichannel image restoration approach is proposed, which is based on the minimum multichannel regularized noise power criterion. Furthermore, no prior knowledge about the variance of the noise at each channel and a bound on the high frequency energy of the image are assumed, but this information is estimated based on the partially restored result at each step. The multichannel smoothing functional to be minimized is formulated to have a global minimizer with the proper choice of the multichannel regularization functionals. With this algorithm, the regularization functional for each channel is determined by incorporating not only within-channel information but also cross-channel information. It is also shown that the proposed multichannel smoothing functional is convex, and therefore, has a global minimizer. The proposed multichannel algorithm not only does not depend on initial conditions but is also shown to be much more computationally efficient than existing algorithms.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
Number of pages12
Editionp 1
StatePublished - Dec 1 1994
EventVisual Communications and Image Processing '94 - Chicago, IL, USA
Duration: Sep 25 1994Sep 29 1994


OtherVisual Communications and Image Processing '94
CityChicago, IL, USA

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Condensed Matter Physics


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