The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Nov. 11, 2014
Filed:
Jun. 22, 2012
Zhou Yu, Waukesha, WI (US);
Bruno Kristiaan Bernard DE Man, Niskayuna, NY (US);
Jean-baptiste Thibault, Waukesha, WI (US);
Debashish Pal, Waukesha, WI (US);
Lin Fu, Niskayuna, NY (US);
Charles A. Bouman, West Lafayette, IN (US);
Ken Sauer, South Ben, IN (US);
Sathish Ramani, Ann Arbor, MI (US);
Jeffrey A. Fessler, Ann Arbor, MI (US);
Somesh Srivastava, Waukesha, WI (US);
Zhou Yu, Waukesha, WI (US);
Bruno Kristiaan Bernard De Man, Niskayuna, NY (US);
Jean-Baptiste Thibault, Waukesha, WI (US);
Debashish Pal, Waukesha, WI (US);
Lin Fu, Niskayuna, NY (US);
Charles A. Bouman, West Lafayette, IN (US);
Ken Sauer, South Ben, IN (US);
Sathish Ramani, Ann Arbor, MI (US);
Jeffrey A. Fessler, Ann Arbor, MI (US);
Somesh Srivastava, Waukesha, WI (US);
General Electric Company, Schenectady, NY (US);
The Regents of the University of Michigan, Ann Arbor, MI (US);
Purdue Research Foundation, West Lafayette, IN (US);
University of Notre Dame du Lac, Notre Dame, IN (US);
Abstract
A method is provided for reconstructing an image of an object that includes image elements. The method includes accessing measurement data associated with the image elements, introducing an auxiliary variable to transform an original problem of reconstructing the image to a constrained optimization problem, and solving the constrained optimization problem using a method of multipliers to create a sequence of sub-problems and solve the sequence of sub-problems. Solving the sequence of sub-problems includes reconstructing the image by optimizing a first objective function. The first objective function is optimized by iteratively solving a nested sequence of approximate optimization problems. An inner loop iteratively optimizes a second objective function approximating the first objective function. An outer loop utilizes the solution of the second objective function to optimize the first objective function.