By Topic

Joint Image Registration and Super-Resolution using Nonlinear Least Squares Method

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Yu He ; Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore ; Kim-Hui Yap ; Li Chen ; Lap-Pui Chau

This paper proposes a new algorithm to integrate image registration into image super-resolution (SR) by fusing multiple blurred low-resolution (LR) images to render a high-resolution (HR) image. Conventional super-resolution (SR) image reconstruction algorithms assume either the estimated motion (displacement) errors by existing registration methods are negligible or the displacement is known a priori. This assumption, however, is impractical as the performance of existing registration algorithms is still less than perfect. In view of this, we present a new estimation framework that performs joint image registration and HR reconstruction. An iterative scheme based on nonlinear least squares method is developed to estimate the motion shift (displacement) and HR image progressively. The motion model that is considered in this work includes both translation as well as rotation. Experimental results show that the proposed method is effective in performing image super-resolution.

Published in:

Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on  (Volume:1 )

Date of Conference:

15-20 April 2007