Milano Retinexes are spatial color algorithms grounded on the Retinex theory and widely applied to enhance the visual content of real-world color images. In this framework, they process the color channels of the input image independently and re-scale channel by channel the intensity of each pixel p by the so-called local reference white, i.e., a strictly positive value, computed by reworking a set of features sampled around p. The neighborhood of p to be sampled, its sampling, the features to be processed, as well as the mathematical model for the computation of the local reference white vary from algorithm to algorithm, determining different levels of enhancement. Based on the analysis of a group of Milano Retinexes, this work proves that the Milano Retinex local reference whites can be expressed by a generalized equation whose parameters model specific aspects of the Milano Retinex spatial color processing. In particular, tuning these parameters leads to different Milano Retinex implementations. This study contributes to a better understanding of the similarities and differences among the members of the Milano Retinex family, and provides new taxonomic schemes of them based on their own mathematical properties.

中文翻译：

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Milano Retinex系列的真实世界图像增强的广义方程。

Milano Retinexes是一种基于Retinex理论的空间色彩算法，已广泛应用于增强现实世界彩色图像的视觉内容。在此框架中，它们独立处理输入图像的色彩通道，并通过所谓的局部参考白色（即严格的正值），通过重新加工一组特征来计算每个像素p的强度，从而重新缩放通道在p周围采样。要采样的p的邻域，其采样，要处理的特征以及用于计算局部参考白色的数学模型因算法而异，从而确定了不同的增强级别。根据一组米兰视网膜群的分析，这项工作证明Milano Retinex本地参考白色可以由广义方程表示，该方程的参数可以模拟Milano Retinex空间色彩处理的特定方面。特别是，调整这些参数会导致使用不同的Milano Retinex实现。这项研究有助于更好地了解Milano Retinex家族成员之间的异同，并根据其自身的数学特性提供新的分类方案。