Globally optimal smoothing functional for multichannel image restoration

Moon Gi Kang; Aggelos K. Katsaggelos

doi:10.1117/12.185965

16 September 1994 Globally optimal smoothing functional for multichannel image restoration

Proceedings Volume 2308, Visual Communications and Image Processing '94; (1994) https://doi.org/10.1117/12.185965
Event: Visual Communications and Image Processing '94, 1994, Chicago, IL, United States

Abstract

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.

Citation Download Citation

Moon Gi Kang and Aggelos K. Katsaggelos "Globally optimal smoothing functional for multichannel image restoration", Proc. SPIE 2308, Visual Communications and Image Processing '94, (16 September 1994); https://doi.org/10.1117/12.185965

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KEYWORDS

Image restoration

Image filtering

Linear filtering

Bandpass filters

Image sensors

Matrices

Error analysis

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