Normalized Total Gradient: A New Measure for Multispectral Image Registration

Chen Shu-Jie Chen, Shen Hui-Liang Shen, Li Chunguang Li, John H. Xin

Research output: Journal article publicationJournal articleAcademic researchpeer-review

26 Citations (Scopus)

Abstract

Image registration is a fundamental issue in multispectral image processing, and is challenged by two main characteristics of multispectral images. First, the regional intensities can be essentially different between band images. Second, the local contrasts of two difference band images are inconsistent or even reversed. Conventional measures can align images with different regional intensity levels, but may fail in the circumstance of severe local intensity variation. In this paper, a new measure called normalized total gradient is proposed for multispectral image registration. The measure is based on the key assumption (observation) that the gradient of the difference between two aligned band images is sparser than that between two misaligned ones. A registration framework, which incorporates image pyramid and global/local optimization, is further introduced for affine transform. Experimental results validate that the proposed method is not only effective for multispectral image registration, but also applicable to general unimodal/multimodal image registration tasks. It performs better than or comparable to the existing methods, both quantitatively and qualitatively.

Original languageEnglish
Pages (from-to)1297-1310
Number of pages14
JournalIEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Volume27
Issue number3
DOIs
Publication statusPublished - 1 Mar 2018

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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