All-Pass Parametric Image Registration

Xinxin Zhang, Christopher Gilliam, Thierry Blu

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

Image registration is a required step in many practical applications that involve the acquisition of multiple related images. In this paper, we propose a methodology to deal with both the geometric and intensity transformations in the image registration problem. The main idea is to modify an accurate and fast elastic registration algorithm (Local All-Pass - LAP) so that it returns a parametric displacement field, and to estimate the intensity changes by fitting another parametric expression. Although we demonstrate the methodology using a low-order parametric model, our approach is highly flexible and easily allows substantially richer parametrisations, while requiring only limited extra computation cost. In addition, we propose two novel quantitative criteria to evaluate the accuracy of the alignment of two images ('salience correlation') and the number of degrees of freedom ('parsimony') of a displacement field, respectively. Experimental results on both synthetic and real images demonstrate the high accuracy and computational efficiency of our methodology. Furthermore, we demonstrate that the resulting displacement fields are more parsimonious than the ones obtained in other state-of-the-art image registration approaches.

Original languageEnglish
Article number9058990
Pages (from-to)5625-5640
Number of pages16
JournalIEEE Transactions on Image Processing
Volume29
DOIs
Publication statusPublished - 2020

Bibliographical note

Funding Information:
Manuscript received March 27, 2019; revised October 11, 2019 and February 8, 2020; accepted March 19, 2020. Date of publication April 7, 2020; date of current version April 20, 2020. This work was supported in part by the Hong Kong Research Grants Council under Grant CUHK14207718. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Yannick Berthoumieu. (Corresponding author: Xinxin Zhang.) Xinxin Zhang and Thierry Blu are with the Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong (e-mail: xxzhang@ee.cuhk.edu.hk; thierry.blu@m4x.org).

Publisher Copyright:
© 2020 IEEE.

Keywords

  • geometric transformation
  • Image registration
  • intensity transformation
  • local all-pass filters
  • parametric fitting
  • registration evaluation

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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