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Neural network‐based image restoration using scaled residual with space‐variant regularization
Author(s) -
Salari E.,
Zhang S.
Publication year - 2002
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.10034
Subject(s) - residual , image restoration , regularization (linguistics) , computer science , image (mathematics) , artificial intelligence , artificial neural network , convergence (economics) , algorithm , computer vision , image processing , economic growth , economics
Image restoration is aimed to recover the original scene from its degraded version. This paper presents a new method for image restoration. In this technique, an evaluation function which combines a scaled residual with space‐variant regularization is established and minimized using a Hopfield network to obtain a restored image from a noise corrupted and blurred image. Simulation results demonstrate that the proposed evaluation function leads to a more efficient restoration process which offers a fast convergence and improved restored image quality. © 2003 Wiley Periodicals, Inc. Int J Imaging Syst Technol 12, 247–253, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.10034