ORCID Profile
0000-0003-4932-0593
Current Organisation
The Hong Kong Polytechnic University
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Publisher: IEEE
Date: 12-2011
Publisher: Springer Science and Business Media LLC
Date: 27-11-2010
Publisher: IEEE
Date: 05-2009
Publisher: IEEE
Date: 06-2008
Publisher: IEEE
Date: 03-2008
Publisher: IEEE
Date: 04-2007
Publisher: Springer Science and Business Media LLC
Date: 25-02-2019
Publisher: IEEE
Date: 05-2011
Publisher: IEEE
Date: 05-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2011
Publisher: Elsevier BV
Date: 10-2017
Publisher: IEEE
Date: 09-2011
Publisher: IEEE
Date: 10-2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2011
Publisher: IEEE
Date: 07-2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2010
Publisher: IEEE
Date: 05-2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2007
Publisher: IEEE
Date: 12-2009
Publisher: IEEE
Date: 05-2009
Publisher: IEEE
Date: 12-2018
Publisher: IEEE
Date: 09-2011
Publisher: Elsevier BV
Date: 04-2010
Publisher: IEEE
Date: 06-2008
Publisher: IEEE
Date: 09-2010
Publisher: IEEE
Date: 07-2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2008
Publisher: IEEE
Date: 05-2009
Publisher: IEEE
Date: 2005
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2007
Abstract: This paper proposes a new algorithm to integrate image registration into image super-resolution (SR). Image SR is a process to reconstruct a high-resolution (HR) image by fusing multiple low-resolution (LR) images. A critical step in image SR is accurate registration of the LR images or, in other words, effective estimation of motion parameters. Conventional SR algorithms assume either the estimated motion parameters by existing registration methods to be error-free or the motion parameters are known a priori. This assumption, however, is impractical in many applications, as most existing registration algorithms still experience various degrees of errors, and the motion parameters among the LR images are generally unknown a priori. In view of this, this paper presents a new framework that performs simultaneous image registration and HR image reconstruction. As opposed to other current methods that treat image registration and HR reconstruction as disjoint processes, the new framework enables image registration and HR reconstruction to be estimated simultaneously and improved progressively. Further, unlike most algorithms that focus on the translational motion model, the proposed method adopts a more generic motion model that includes both translation as well as rotation. An iterative scheme is developed to solve the arising nonlinear least squares problem. Experimental results show that the proposed method is effective in performing image registration and SR for simulated as well as real-life images.
Publisher: IEEE
Date: 05-2019
Publisher: IEEE
Date: 12-2009
Publisher: IEEE
Date: 03-2008
Publisher: Elsevier BV
Date: 03-2009
No related grants have been discovered for Lap-Pui Chau.