A Novel Multi-Perspective Benchmarking Framework for Selecting Image Dehazing Intelligent Algorithms Based on BWM and Group VIKOR Techniques
Abstract
The increasing demand for image dehazing-based applications has raised the value of efficient evaluation and benchmarking for image dehazing algorithms. Several perspectives, such as inhomogeneous foggy, homogenous foggy, and dark foggy scenes, have been considered in multi-criteria evaluation. The benchmarking for the selection of the best image dehazing intelligent algorithm based on multi-criteria perspectives is a challenging task owing to (a) multiple evaluation criteria, (b) criteria importance, (c) data variation, (d) criteria conflict, and (e) criteria tradeoff. A generally accepted framework for benchmarking image dehazing performance is unavailable in the existing literature. This study proposes a novel multi-perspective (i.e., an inhomogeneous foggy scene, a homogenous foggy scene, and a dark foggy scene) benchmarking framework for the selection of the best image dehazing intelligent algorithm based on multi-criteria analysis. Experiments were conducted in three stages. First was an evaluation experiment with five algorithms as part of matrix data. Second was a crossover between image dehazing intelligent algorithms and a set of target evaluation criteria to obtain matrix data. Third was the ranking of the image dehazing intelligent algorithms through integrated best–worst and VIseKriterijumska Optimizacija I Kompromisno Resenje methods. Individual and group decision-making contexts were applied to demonstrate the efficiency of the proposed framework. The mean was used to objectively validate the ranks given by group decision-making contexts. Checklist and benchmarking scenarios were provided to compare the proposed framework with an existing benchmark study. The proposed framework achieved a significant result in terms of selecting the best image dehazing algorithm.
