Multichannel image restoration based on optimization of the structural similarity index

Authors:
Maja Temerinac-Ott;Hans Burkhardt
Affiliations:
Institute of Computer Science, University of Freiburg, Pattern Recognition and Image Processing, Freiburg, Germany and Centre for Biological Signalling Studies, University of Freiburg, Germany;Institute of Computer Science, University of Freiburg, Pattern Recognition and Image Processing, Freiburg, Germany and Centre for Biological Signalling Studies, University of Freiburg, Germany
Venue:
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Year:
2009

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Abstract

In this paper a framework for multichannel image restoration based on optimization of the structural similarity (SSIM) index is presented. The SSIM index describes the similarity of images more appropriately for the human visual system than the mean square error (MSE). It has not yet been explored for the multi channel restoration task. The construction of an optimization algorithm is difficult due to the non-linearity of the SSIM measure. The existing solution based on a quasi-convex problem formulation is successfully extended for the multichannel image restoration. The correctness of the algorithm is verified on sample images and it is shown that multi-view information can significantly improve the restoration results.