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.
Highlights
One of the most critical aspects of many applications in image processing and computer vision, including SuperResolution, is the accurate estimation of motion, known as image registration
The motion parameters are the unknowns in the approximation, and they can be computed from the set of equations that can be derived from this approximation
In the first part of this section we will mention the experimental results for image registration using synthesized data
Summary
One of the most critical aspects of many applications in image processing and computer vision, including SuperResolution, is the accurate estimation of motion, known as image registration. The low-resolution (LR) images may be noisy and blurred and have some displacements with each other. These methods utilize information from multiple observed images to achieve restoration at resolutions higher than that of the original data. In SR literatures a variety of registration approaches have been presented They can be classified into two main approaches: feature-based methods and area-based methods. One of the famous registration method is the pioneering work of Lucas and Kanade [4]. This is an area-based method which is based on using of a Taylor series approximation of the images. Baker et al [5] introduced a unified framework for Lucas-Kanade algorithm, and we will use their formulation for explaining our method in the rest of this paper
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
