Regularized total least squares approach for nonconvolutional linear inverse problems

Wenwu Zhu, Yao Wang, Nikolas P. Galatsanos, Jun Zhang

Research output: Contribution to journalArticlepeer-review


In this correspondence, a solution is developed for the regularized total least squares (RTLS) estimate in linear inverse problems where the linear operator is nonconvolutional. Our approach is based on a Rayleigh quotient (RQ) formulation of the TLS problem, and we accomplish regularization by modifying the RQ function to enforce a smooth solution. A conjugate gradient algorithm is used to minimize the modified RQ function. As an example, the proposed approach has been applied to the perturbation equation encountered in optical tomography. Simulation results show that this method provides more stable and accurate solutions than the regularized least squares and a previously reported total least squares approach, also based on the RQ formulation.

Original languageEnglish (US)
Pages (from-to)1657-1661
Number of pages5
JournalIEEE Transactions on Image Processing
Issue number11
StatePublished - 1999


  • Image reconstruction
  • Image recovery
  • Image restoration
  • Inverse problems
  • Optical tomography
  • Regularization
  • Tomographic imaging

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


Dive into the research topics of 'Regularized total least squares approach for nonconvolutional linear inverse problems'. Together they form a unique fingerprint.

Cite this