论文标题
将快速的X射线荧光栅格扫描绘画
Denoising Fast X-Ray Fluorescence Raster Scans of Paintings
论文作者
论文摘要
宏X射线荧光（XRF）对文化遗产对象的成像，而流行的非侵入性技术提供了元素分布图，是获得高信噪比XRF体积的缓慢获取过程。通常，在每个像素的十分之一的顺序上，栅格扫描探针计算X射线照明下对象发出的不同能量的光子数量。为了减少扫描时间而不牺牲元素图和XRF音量质量，我们建议使用Poisson噪声模型和基于颜色图像的词典学习在恢复嘈杂，快速获取的XRF数据之前使用字典学习。
Macro x-ray fluorescence (XRF) imaging of cultural heritage objects, while a popular non-invasive technique for providing elemental distribution maps, is a slow acquisition process in acquiring high signal-to-noise ratio XRF volumes. Typically on the order of tenths of a second per pixel, a raster scanning probe counts the number of photons at different energies emitted by the object under x-ray illumination. In an effort to reduce the scan times without sacrificing elemental map and XRF volume quality, we propose using dictionary learning with a Poisson noise model as well as a color image-based prior to restore noisy, rapidly acquired XRF data.
