Missing intensity restoration via perceptually optimized subspace projection based on entropy component analysis | IEEE Conference Publication | IEEE Xplore

Missing intensity restoration via perceptually optimized subspace projection based on entropy component analysis


Abstract:

A missing intensity restoration method via perceptually optimized subspace projection based on entropy component analysis (ECA) is presented in this paper. The proposed m...Show More

Abstract:

A missing intensity restoration method via perceptually optimized subspace projection based on entropy component analysis (ECA) is presented in this paper. The proposed method calculates the optimal subspace of known patches within a target image based on structural similarity (SSIM) index, and the optimal bases are determined based on ECA. Then missing intensity estimation whose results maximize the SSIM index is realized by using a projection onto convex sets (POCS) algorithm whose constraints are the obtained subspace and known intensities within the target image. In this approach, a non-convex maximization problem for calculating the projection onto the subspace is reformulated as a quasi-convex problem, and the restoration of the missing intensities becomes feasible. Experimental results show that our restoration method outperforms previously reported methods.
Date of Conference: 04-09 May 2014
Date Added to IEEE Xplore: 14 July 2014
Electronic ISBN:978-1-4799-2893-4

ISSN Information:

Conference Location: Florence, Italy

References

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