Open AccessImage fusion based on multiscale transform and sparse representation to enhance terahertz images
Author(s) -
Qi Mao,
Yanqiu Zhu,
Cixing Lv,
Yao Lu,
Xiaohui Yan,
Dongshan Wei,
Shihan Yan,
Jingbo Liu
Publication year - 2020
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.396604
Subject(s) - image fusion , artificial intelligence , terahertz radiation , curvelet , wavelet transform , sparse approximation , computer science , image resolution , computer vision , pyramid (geometry) , complex wavelet transform , wavelet , optics , materials science , pattern recognition (psychology) , discrete wavelet transform , image (mathematics) , physics
High-quality terahertz (THz) images are vital to integrated circuit (IC) manufacturing. Due to the unique sensitivity of THz waves to different materials, the images obtained from the point-spread function (PSF) model have fewer image details and less texture information in some frequency bands. This paper presents an image fusion technique to enhance the resolution of THz IC images. The source images obtained from the PSF model are processed by a fusion method combining a multiscale transform (MST) and sparse representation (SR). The low-pass band is handled by sparse representation, and the high-pass band is fused by the conventional "max-absolute" rule. From both objective and visual perspectives, four popular multiscale transforms-the Laplacian pyramid, the ratio of low-pass pyramids, the dual-tree complex wavelet transform and the curvelet transform-are thoroughly compared at different decomposition levels ranging from one to four. This work demonstrates the feasibility of using image fusion to enhance the resolution of THz IC images.