A hybrid projective method for solving system of equilibrium problems with demicontractive mappings applicable in image restoration problems

Abstract

In this paper, we propose a general iterative scheme based on CQ projection method for finding a common solution of system of equilibrium problems and the fixed point set of a finite family of demicontractive mappings. We also prove strong convergence of the scheme to a common element of the two above‐described sets. We then give a numerical example to justify our main result. An example is given in an infinite dimensional space for supporting our main result. Moreover, we apply our main result to solve the unconstrained image restoration problems with a finite family of blurring operators. Our results extend and improve some existing results in the literature.

Inertial hybrid algorithm for generalized mixed equilibrium problems, zero problems, and fixed points of some nonlinear mappings in the intermediate sense

“…Our Theorem 1 provides generalizations and improvements on the results of Khan et al [44] in the following ways:…”

“…1. In Khan et al [44], systems of classical equilibrium problems (18), which are special cases of the problems described in (14) and finite family of demicontractive mappings are considered, while in our result, systems of generalized mixed equilibrium problems ( 14) and a countable family of equally continuous and asymptotically demicontractive mappings in the intermediate sense are studied. 2.…”

“…2. Apart from determining solutions to fixed point and equilibrium problems by the Algorithm of Khan et al [44], our scheme is also used to approximate zeros of a family of mappings satisfying (10). 3.…”

“…Very recently, Khan et al [44] proposed a method based on the scheme (19) of Nakajo and Takahashi, for equilibrium problems and fixed point of demicontractives. They recursively defined their iterative algorithm as follows;…”

Multilabel land cover aerial image classification using convolutional neural networks

“…CNN (Fukushima et al 1983) in recent years has become more popular as it shows its excellence in image classification (He et al 2015;Liu et al 2017), facial recognition (Sun et al 2014), target object detection (Ouyang et al 2015), lane detection, or obstacle detection or pedestrian detection (Ouyang et al 2017). Neural network is applied in diversified fields for image categorizations and analysis (Khan et al 2020;Kim et al 2020;Sanchez Lasheras et al 2020). The best part of CNN is that it performs effectively when applied to remote sensing images.…”