Subpixel image registration regularised by l1 and l2 norms. Issue 12 (8th September 2020)

• Record Type: Journal Article
• Authors: Huang, Qirui; Yang, Xuan
• Is Part Of: IET image processing. Volume 14:Issue 12(2020)
• Page Start: 2845
• Page End: 2854
• Publication Date: 2020-09-08
• Subjects: image registration -- medical image processing -- brain -- iterative methods; nonrigid registration models -- rigid registration models -- subpixel image registration framework -- fundus image registration data -- nonrigid subpixel image registration models -- rigid subpixel image registration models -- transformation function -- compact support radial basis functions -- nonrigid registration model -- rigid transformation parameters -- rigid registration model -- subpixel image registration problem; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.1384
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : In this study, the authors propose a subpixel image registration framework that detects and matches feature points. Rigid and nonrigid registration models are employed to solve the problem of subpixel image registration problem. A rigid registration model based on the l 2 norm is proposed to regularise the rotation coefficients using the indicator function to estimate the rigid transformation parameters. The latter estimation simplified is made easy by the reduction in the rigid transformation from two dimensions to one dimension. Furthermore, a non‐rigid registration model based on the l 1 and l 2 norms is proposed to estimate the elastic coefficients of the compact support radial basis functions. Due to the linear representation of the transformation function, the rigid and nonrigid subpixel image registration models can be solved efficiently using the fast iterative shrinkage‐thresholding algorithm. Experiments on a demosaicing data set, the ocean of remote sensing data set, a brain data set and the fundus image registration data set show that the proposed rigid and non‐rigid registration models can accurately perform subpixel image registration.