An Efficient Iterative Algorithm for Solving the Split Feasibility Problem in Hilbert Spaces Applicable in Image Deblurring, Signal Recovering, and Polynomiography
Copyright policy )An Efficient Iterative Algorithm for Solving the Split Feasibility Problem in Hilbert Spaces Applicable in Image Deblurring, Signal Recovering, and Polynomiography
The split feasibility problem (SFP) in Hilbert spaces is addressed in this study using an efficient iterative approach. Under mild conditions, we prove convergence theorems for the algorithm for finding a solution to the SFP. We also present numerical examples to illustrate that the acceleration of our algorithm is effective. Our results are applied to solve image deblurring and signal recovery problems. Furthermore, we show the use of the proposed method to generate polynomiographs.
Inverse Problems in Mathematical Physics and Imaging, Economics, Bilevel Programming, Inverse Problems, Acceleration, Computational Mechanics, Fixed-Point Problems, Mathematical analysis, Sparse Approximation, Image restoration, Engineering, Image processing, QA1-939, FOS: Mathematics, Image (mathematics), Classical mechanics, Iterative Algorithms, Mathematical Physics, Economic growth, Signal recovery, Physics, Mathematical optimization, Hilbert space, Iterative Algorithms for Nonlinear Operators and Optimization, Theory and Applications of Compressed Sensing, Applied mathematics, Computer science, Iterative method, Algorithm, Computational Theory and Mathematics, Deblurring, Computer Science, Physical Sciences, Convergence (economics), Compressed sensing, Computer vision, Mathematics
Inverse Problems in Mathematical Physics and Imaging, Economics, Bilevel Programming, Inverse Problems, Acceleration, Computational Mechanics, Fixed-Point Problems, Mathematical analysis, Sparse Approximation, Image restoration, Engineering, Image processing, QA1-939, FOS: Mathematics, Image (mathematics), Classical mechanics, Iterative Algorithms, Mathematical Physics, Economic growth, Signal recovery, Physics, Mathematical optimization, Hilbert space, Iterative Algorithms for Nonlinear Operators and Optimization, Theory and Applications of Compressed Sensing, Applied mathematics, Computer science, Iterative method, Algorithm, Computational Theory and Mathematics, Deblurring, Computer Science, Physical Sciences, Convergence (economics), Compressed sensing, Computer vision, Mathematics
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