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COMBINING REGULARIZATION FRAMEWORKS FOR IMAGE DEBLURRING: OPTIMIZATION OF COMBINED HYPER-PARAMETERS
 

Summary: COMBINING REGULARIZATION FRAMEWORKS FOR IMAGE
DEBLURRING: OPTIMIZATION OF COMBINED HYPER-PARAMETERS
R. Youmaran and A. Adler
youmaran@site.uottawa.ca, adler@site.uottawa.ca,
School of Information Technology and Engineering (SITE)
University Of Ottawa
Abstract Regularization is an important tool for
restoration of images from noisy and blurred data. In
this paper, we present a novel regularization technique
(CGTik) that augments the conjugate gradient least-
square (CGLS) algorithm with Tikhonov-like prior
information term. This technique requires the
appropriate selection of two hyper-parameters, the
number of iterations (N) and amount of regularization
(). A method to select good values for these
parameters is developed based on the L-curve
technique. Tests were performed by calculating
reconstructed images for each algorithm for heavily
blurred images. CGTik showed improved restored
images compared to the separate algorithms Tikhonov

  

Source: Adler, Andy - Department of Systems and Computer Engineering, Carleton University

 

Collections: Computer Technologies and Information Sciences