Compressive tomography consists of estimation of high dimensional objects from measurements distributed over lower dimensions. Examples include reconstruction of 3D spectral data cubes from 2D focal planes and reconstruction of 3D volumes from 2D x-ray projections or holograms. Compressive tomographic estimation is improved if projections are structured to randomize the sampling phase space. To illustrate this principle, we show that structured x-ray illumination enables improvements in reconstructed image quality for compressed measurements relative to full Radon sampling and that structured millimeter wave illumination improves estimation of 3D surfaces.

© 2014 Optical Society of America

PDF Article
More Like This
Compressive X–ray phase tomography based intensity transpart

Lei Tian, Jonathan C. Petruccelli, Qin Miao, and George Barbastathis
CTu2C.4 Computational Optical Sensing and Imaging (COSI) 2013

Phase Imaging with X-ray Digital Holography and Compressive Sensing Approach

Shakil Rehman, Duan Yubo, Chen Wensheng, Kiyofumi Matsuda, and George Barbastathis
DTh2B.1 Digital Holography and Three-Dimensional Imaging (DH) 2014

Soft X-ray Holographic Tomography for Biological Specimens

Hongyi Gao, Jianwen Chen, Honglan Xie, Ruxin Li, Zhizhan Xu, Shiping Jiang, and Yuxuan Zhang
5143_215 European Conference on Biomedical Optics (ECBO) 2003