In many situations, an expert must visually analyze an image arranged in grey levels. However, the human eye has strong difficulty in detecting details in this type of image, making it necessary to use artificial coloring techniques. The pseudo-coloring problem (PsCP) consists of assigning to a grey-level image, pre-segmented in K sub-regions, a set of K colors that are as dissimilar as possible. This problem is part of the well-known class of NP-Hard problems and, therefore, does not present an exact solution for all instances. Thus, meta-heuristics has been widely used to overcome this problem. In particular, genetic algorithm (GA) is one of those techniques that stands out in the literature and has already been used in PsCP. In this work, we present a new method that consists of an improvement of the GA specialized in solving the PsCP. In addition, we propose the addition of local search operators and rules for adapting parameters based on symmetric mapping functions to avoid common problems in this type of technique such as premature convergence and inadequate exploration in the search space. Our method is evaluated in three different case studies: the first consisting of the pseudo-colorization of real-world images on the RGB color space; the second consisting of the pseudo-colorization in RGB color space considering synthetic and abstract images in which its sub-regions are fully-connected; and the third consisting of the pseudo-colorization in the Munsell atlas color set. In all scenarios, our method is compared with other state-of-the-art techniques and presents superior results. Specifically, the use of mapped automatic adjustment operators proved to be powerful in boosting the proposed meta-heuristic to obtain more robust results in all evaluated instances of PsCP in all the considered case studies.

中文翻译：

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一种用于伪着色问题的具有新映射自适应算子的改进局部搜索遗传算法

在许多情况下，专家必须目视分析按灰度级排列的图像。然而，人眼在这种类型的图像中检测细节的难度很大，因此必须使用人工着色技术。伪着色问题 (PsCP) 包括为预分割在 K 个子区域中的灰度图像分配一组 K 种尽可能不同的颜色。这个问题是众所周知的 NP-Hard 问题类别的一部分，因此并没有为所有实例提供精确的解决方案。因此，元启发式已被广泛用于克服这个问题。特别是，遗传算法 (GA) 是在文献中脱颖而出并已在 PsCP 中使用的技术之一。在这项工作中，我们提出了一种新方法，包括改进专门用于解决 PsCP 的 GA。此外，我们建议添加局部搜索算子和基于对称映射函数的参数适应规则，以避免此类技术中的常见问题，例如过早收敛和搜索空间探索不足。我们的方法在三个不同的案例研究中进行了评估：第一个包括 RGB 颜色空间上真实世界图像的伪着色；第二个由 RGB 颜色空间中的伪着色组成，考虑到其子区域完全连接的合成和抽象图像；第三个由孟塞尔地图集颜色集中的伪着色组成。在所有情况下，我们的方法都与其他最先进的技术进行了比较，并呈现出优异的结果。具体来说，