Abstract
Recent studies have demonstrated that the Structural Similarity Index Measure (SSIM) is the top choice for quantifying both visual quality and image similarity. Although the SSIM is not convex, it has been successfully employed in a wide range of imaging tasks over the last years. In this paper, the authors propose a new method based on the Alternate Direction Method of Multipliers (ADMM) for solving an unconstrained SSIM-based optimization problem. We focus our analysis on the case in which the regularizing term is convex. The paper also includes numerical examples and experiments that showcase the effectiveness of the proposed method.
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Acknowledgements
This work has been supported in part by Discovery Grants (ERV and OM) from the Natural Sciences and Engineering Research Council of Canada (NSERC). Financial support from the Faculty of Mathematics and the Department of Applied Mathematics, University of Waterloo (DO) is also gratefully acknowledged.
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Otero, D., Torre, D.L., Michailovich, O.V., Vrscay, E.R. (2018). Alternate Direction Method of Multipliers for Unconstrained Structural Similarity-Based Optimization. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds) Image Analysis and Recognition. ICIAR 2018. Lecture Notes in Computer Science(), vol 10882. Springer, Cham. https://doi.org/10.1007/978-3-319-93000-8_3
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