Given the rapid development of dehazing image algorithms, selecting the optimal algorithm based on multiple criteria is crucial in determining the efficiency of an algorithm. However, a sufficient number of criteria must be considered when selecting an algorithm in multiple foggy scenes, including inhomogeneous, homogenous and dark foggy scenes. However, the selection of an optimal real-time image dehazing algorithm based on standardised criteria presents a challenge. According to previous studies, a standardisation and selection framework for real-time image dehazing algorithms based on multi-foggy scenes is not yet available. To address this gap, this study proposes a new standardisation and selection framework based on fuzzy Delphi (FDM) and hybrid multi-criteria analysis methods. Experiments are also conducted in three phases. Firstly, the image dehazing criteria are standardised based on FDM. Secondly, an evaluation experiment is conducted based on standardised criteria and nine real-time image dehazing algorithms to obtain a multi-perspective matrix. Third, entropy and VIKOR methods are hybridised to determine the weight of the standardised criteria and to rank the algorithms. Three rules are applied in the standardisation process to determine the criteria. To objectively validate the selection results, mean is applied for this purpose. The results of this work can be taken into account in designing efficient methods and metrics for image dehazing.

鉴于去雾图像算法的快速发展，基于多个标准选择最佳算法对于确定算法的效率至关重要。但是，在多个有雾的场景中选择算法时，必须考虑足够多的标准，包括不均匀，同质和黑暗的有雾场景。然而，基于标准化标准的最优实时图像去雾算法的选择提出了挑战。根据以前的研究，基于多雾场景的实时图像去雾算法的标准化和选择框架尚不可用。为了弥补这一差距，本研究提出了一种基于模糊德尔菲（FDM）和混合多准则分析方法的新的标准化和选择框架。实验也分三个阶段进行。首先，图像去雾标准是基于FDM标准化的。其次，基于标准化标准和九种实时图像去雾算法进行评估实验，以获得多视角矩阵。第三，将熵和VIKOR方法进行混合，以确定标准化标准的权重并对算法进行排名。在标准化过程中应用了三个规则来确定标准。为了客观地验证选择结果，将均值用于此目的。在设计有效的图像去雾方法和度量标准时，可以考虑这项工作的结果。熵和VIKOR方法进行混合以确定标准的权重并对算法进行排名。在标准化过程中应用了三个规则来确定标准。为了客观地验证选择结果，将均值用于此目的。在设计有效的图像去雾方法和度量标准时，可以考虑这项工作的结果。熵和VIKOR方法进行混合以确定标准的权重并对算法进行排名。在标准化过程中应用了三个规则来确定标准。为了客观地验证选择结果，将均值用于此目的。在设计有效的图像去雾方法和度量标准时，可以考虑这项工作的结果。