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Generalized inertial proximal deblurring

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journal contribution
posted on 2025-01-23, 10:22 authored by Y Savoye, D Yambangwai, W Cholamjiak

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.

History

Author affiliation

College of Science & Engineering Comp' & Math' Sciences

Version

  • AM (Accepted Manuscript)

Published in

Journal of Mathematics and Computer Science

Volume

37

Issue

02

Pagination

167 - 189

Publisher

International Scientific Research Publications MY SDN. BHD.

eissn

2008-949X

Copyright date

2024

Available date

2025-01-23

Language

en

Deposited by

Dr Yann Savoye

Deposit date

2024-09-24

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