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Multichannel Image Restoration Based on Optimization of the Structural Similarity Index
 

Summary: Multichannel Image Restoration
Based on Optimization of the Structural Similarity Index
Maja Temerinac-Ott and Hans Burkhardt
Institute of Computer Science, University of Freiburg,
Chair of Pattern Recognition and Image Processing, Georges-K¨ohler-Allee Geb. 052,
79110 Freiburg, Germany, temerina@informatik.uni-freiburg.de
Centre for Biological Signalling Studies (bioss), University of Freiburg, Germany
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.

  

Source: Albert-Ludwigs-Universität Freiburg, Institut für Informatik,, Lehrstuhls für Mustererkennung und Bildverarbeitung

 

Collections: Computer Technologies and Information Sciences