An Efficient Iterative Algorithm for Solving the Split Feasibility Problem in Hilbert Spaces Applicable in Image Deblurring, Signal Recovering, and Polynomiography

Abstract

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.