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Total Variation Minimization with Separable Sensing Operator
 

Summary: Total Variation Minimization
with Separable Sensing Operator
Serge L. Shishkin, Hongcheng Wang, and Gregory S. Hagen
United Technologies Research Center, 411 Silver Ln, MS 129-15, East Hartford, CT
06108, USA
Abstract. Compressed Imaging is the theory that studies the problem
of image recovery from an under-determined system of linear measure-
ments. One of the most popular methods in this field is Total Variation
(TV) Minimization, known for accuracy and computational efficiency.
This paper applies a recently developed Separable Sensing Operator ap-
proach to TV Minimization, using the Split Bregman framework as the
optimization approach. The internal cycle of the algorithm is performed
by efficiently solving coupled Sylvester equations rather than by an it-
erative optimization procedure as it is done conventionally. Such an ap-
proach requires less computer memory and computational time than any
other algorithm published to date. Numerical simulations show the im-
proved -- by an order of magnitude or more -- time vs. image quality
compared to two conventional algorithms.
1 Introduction
Compressed Imaging (CI) methods perform image recovery from a seemingly

  

Source: Ahuja, Narendra - Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign

 

Collections: Computer Technologies and Information Sciences; Engineering