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首页> 外文期刊>International Journal of Information Technology & Decision Making >A Novel Multi-Perspective Benchmarking Framework for Selecting Image Dehazing Intelligent Algorithms Based on BWM and Group VIKOR Techniques
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A Novel Multi-Perspective Benchmarking Framework for Selecting Image Dehazing Intelligent Algorithms Based on BWM and Group VIKOR Techniques

机译：基于BWM和Group Vikor技术选择脱色智能算法的新型多透视基准框架

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摘要

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 multicriteria 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.
机译：对基于图像脱水的应用的不断增加提高了图像脱水算法的有效评估和基准的价值。在多准则评估中，已经考虑了几种观点，例如不均匀的雾，均匀的雾和暗雾场景。基于多标准观点的基于多标准观点选择的基准测试是由于（a）多评价标准，（b）标准重要性，（c）数据变化，（d）标准冲突，以及（e）标准权衡。通常接受的基准测试性能的框架在现有的文献中不可用。本研究提出了一种新颖的多视角（即非均匀有雾的场景，同质有雾的场景，一个暗面的有雾场景）基准测试框架，用于选择基于多标准分析的最佳图像脱色智能算法。实验是三个阶段进行的。首先是具有五种算法的评估实验，作为矩阵数据的一部分。其次是图像脱色智能算法和一组目标评估标准之间的交叉，以获得矩阵数据。三是通过集成最糟糕的最糟糕和vienskriterijumska OptimizaCija i Kompromisno Resenje方法排名脱色智能算法。个人和小组决策背景被应用于证明拟议框架的效率。均值用于客观地验证由组决策背景给出的等级。提供了清单和基准测试方案，以将拟议的框架与现有的基准研究进行比较。所提出的框架在选择最佳图像去吸附算法方面实现了显着的结果。

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