In this paper we study the application of Compressed Sensing (CS) framework for optical tomography based on the Rytov approximation to the heterogeneous photon diffusion equation. Simulations are performed on a sample system to validate and compare inverse image reconstructions with l1-regularization (CS) and Singular Value Decomposition (SVD) respectively. Potential benefits and shortcomings of CS are discussed and are shown in the context of diffuse optical imaging.

© 2010 Optical Society of America

PDF Article


You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
Login to access OSA Member Subscription