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Precise Image Registration with Structural Similarity Error Measurement Applied to Superresolution

Abstract

Precise image registration is a fundamental task in many computer vision algorithms including superresolution methods. The well known Lucas-Kanade (LK) algorithm is a very popular and efficient method among the various registration techniques. In this paper a modified version of it, based on the Structural Similarity (SSIM) image quality assessment is proposed. The core of the proposed method is contributing the SSIM in the sum of squared difference, which minimized by LK algorithm. Mathematical derivation of the proposed method is based on the unified framework of Baker et al. (2004). Experimental results over 1000 runs on synthesized data validate the better performance of the proposed modification of LK-algorithm, with respect to the original algorithm in terms of the rate and speed of convergence, where the signal-to-noise ratio is low. In addition the result of using the proposed approach in a superresolution application is given.

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Correspondence to Mahmood Amintoosi.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Amintoosi, M., Fathy, M. & Mozayani, N. Precise Image Registration with Structural Similarity Error Measurement Applied to Superresolution. EURASIP J. Adv. Signal Process. 2009, 305479 (2009). https://doi.org/10.1155/2009/305479

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  • DOI: https://doi.org/10.1155/2009/305479

Keywords

  • Image Quality
  • Computer Vision
  • Quality Assessment
  • Efficient Method
  • Quantum Information