Precise Image Registration with Structural Similarity Error Measurement Applied to Superresolution
© Mahmood Amintoosi et al. 2009
Received: 11 November 2008
Accepted: 22 May 2009
Published: 30 June 2009
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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