Abstract
In [447, 449, 450], a constrained optimization type of numerical algorithm for restoring blurry, noisy images was developed and successfully tested. In this paper we present both theoretical and experimental justification for the method. Our main theoretical results involve constrained nonlinear partial differential equations. Our main experimental results involve blurry images which have been further corrupted with multiplicative noise. As in additive noise case of [447, 450] our numerical algorithm is simple to implement and is nonoscillatory (minimal ringing) and noninvasive (recovers sharp edges).
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© 2003 Springer-Verlag New York, Inc.
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Rudin, L., Lions, PL., Osher, S. (2003). Multiplicative Denoising and Deblurring: Theory and Algorithms. In: Geometric Level Set Methods in Imaging, Vision, and Graphics. Springer, New York, NY. https://doi.org/10.1007/0-387-21810-6_6
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DOI: https://doi.org/10.1007/0-387-21810-6_6
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-95488-2
Online ISBN: 978-0-387-21810-6
eBook Packages: Springer Book Archive