Generalized inertial proximal deblurring
Authors
Y. Savoye
- School of Computer Science and Mathematics, University of Leicester, United Kingdom.
D. Yambangwai
- School of Science, University of Phayao, Thailand.
W. Cholamjiak
- School of Science, University of Phayao, Thailand.
Abstract
Visual signal deblurring is a challenging computational problem involving spatially invariant point spread functions, large
blurring matrices and deconvolution. We formulate the visual content restoration process as an inverse convex minimization
problem. We design a novel iterative multi-steps scheme incorporating an inertial term to approximate an element of the set
of solutions of accretive inclusion problems. We generalize our solver for a large variety of inverse problems in imaging such
as convex minimization, variational inequality and split feasibility problems. We compare the convergence rate and perceptual
quality assessment with state-of-the-art algorithms on various visual input data. We demonstrate the effectiveness of our solver
to deblur RGB images, HDR images, height fields, geometry images as well as motion caption data.
Share and Cite
ISRP Style
Y. Savoye, D. Yambangwai, W. Cholamjiak, Generalized inertial proximal deblurring, Journal of Mathematics and Computer Science, 37 (2025), no. 2, 167--189
AMA Style
Savoye Y., Yambangwai D., Cholamjiak W., Generalized inertial proximal deblurring. J Math Comput SCI-JM. (2025); 37(2):167--189
Chicago/Turabian Style
Savoye, Y., Yambangwai, D., Cholamjiak, W.. "Generalized inertial proximal deblurring." Journal of Mathematics and Computer Science, 37, no. 2 (2025): 167--189
Keywords
- Numerical optimization
- visual data
- deblurring
MSC
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