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Genetic algorithm-based parameter selection approach to single image defogging

机译:基于遗传算法的单幅图像除雾参数选择方法

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Image defogging is widely used in many outdoor working systems. However, owing to the Jack of enough information to solve the equation of image degradation model, existing restoration methods generally introduce some parameters and set these values fixed. Inappropriate parameter setting will lead to difficulty in obtaining the best defogging results for different input foggy images. This letter proposes a novel defogging parameter value selection algorithm based on genetic algorithm (GA). We mainly focus on the way to select optimal parameter values for image defogging. The proposed method is applied to two representative defogging algorithms by selecting the two main parameters and optimizing them using the genetic algorithm. An assessment index of image defogging effect is used in the proposed method as the fitness function of the genetic algorithm. Thus, these parameters may be adaptively and automatically adjusted for the defogging algorithms. A comparative study and qualitative evaluation demonstrate that the better quality results are obtained by using the proposed method. (C) 2016 Elsevier B.V. All rights reserved.
机译:图像除雾在许多户外工作系统中被广泛使用。然而,由于杰克拥有足够的信息来解决图像退化模型的方程,现有的恢复方法通常会引入一些参数并将这些值设置为固定值。参数设置不当将导致难以获得不同输入雾图像的最佳除雾效果。这封信提出了一种基于遗传算法(GA)的新型除雾参数值选择算法。我们主要关注为图像除雾选择最佳参数值的方法。通过选择两个主要参数并使用遗传算法对它们进行优化,将该方法应用于两种代表性的除雾算法。该方法将图像除雾效果的评估指标作为遗传算法的适应度函数。因此,这些参数可以针对除雾算法被自适应地并且自动地调整。对比研究和定性评估表明,使用所提出的方法可以获得更好的质量结果。 (C)2016 Elsevier B.V.保留所有权利。

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