Open AccessImage stack alignment in full‐field X‐ray absorption spectroscopy using SIFT_PyOCL
Author(s) -
Paleo Pierre,
Pouyet Emeline,
Kieffer Jérôme
Publication year - 2014
Publication title -
journal of synchrotron radiation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.172
H-Index - 99
ISSN - 1600-5775
DOI - 10.1107/s160057751400023x
Subject(s) - scale invariant feature transform , computer science , artificial intelligence , hyperspectral imaging , computer vision , pixel , image registration , translation (biology) , algorithm , computer graphics (images) , pattern recognition (psychology) , feature extraction , image (mathematics) , chemistry , biochemistry , messenger rna , gene
Full‐field X‐ray absorption spectroscopy experiments allow the acquisition of millions of spectra within minutes. However, the construction of the hyperspectral image requires an image alignment procedure with sub‐pixel precision. While the image correlation algorithm has originally been used for image re‐alignment using translations, the Scale Invariant Feature Transform (SIFT) algorithm (which is by design robust versus rotation, illumination change, translation and scaling) presents an additional advantage: the alignment can be limited to a region of interest of any arbitrary shape. In this context, a Python module, named SIFT_PyOCL, has been developed. It implements a parallel version of the SIFT algorithm in OpenCL, providing high‐speed image registration and alignment both on processors and graphics cards. The performance of the algorithm allows online processing of large datasets.