학술논문
X-Ray fluorescence image super-resolution using dictionary learning
Document Type
Conference
Author
Source
2016 IEEE 12th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP) Image, Video, and Multidimensional Signal Processing Workshop (IVMSP), 2016 IEEE 12th. :1-5 Jul, 2016
Subject
Language
Abstract
X-Ray fluorescence (XRF) scanning of works of art is becoming an increasingly popular non-destructive analytical method. The high quality XRF spectra is necessary to obtain significant information on both major and minor elements used for characterization and provenance analysis. However, there is a trade-off between the spatial resolution of an XRF scan and the Signal-to-Noise Ratio (SNR) of each pixel's spectrum, due to the limited scanning time. In this paper, we propose an XRF image super-resolution method to address this trade-off, thus obtaining a high spatial resolution XRF scan with high SNR. We use a sparse representation of each pixel using a dictionary trained from the spectrum samples of the image, while imposing a spatial smoothness constraint on the sparse coefficients. We then increase the spatial resolution of the sparse coefficient map using a conventional super-resolution method. Finally the high spatial resolution XRF image is reconstructed by the high spatial resolution sparse coefficient map and the trained spectrum dictionary.