Morphological neural networks allow to learn the weights of a structuring function knowing the desired output image. However, those networks are not intrinsically robust to lighting variations in images with an optical cause, such as a change of light intensity. In this paper, we introduce a morphological neural network which possesses such a robustness to lighting variations. It is based on the recent framework of Logarithmic Mathematical Morphology (LMM), i.e. Mathematical Morphology defined with the Logarithmic Image Processing (LIP) model. This model has a LIP additive law which simulates in images a variation of the light intensity. We especially learn the structuring function of a LMM operator robust to those variations, namely : the map of LIP-additive Asplund distances. Results in images show that our neural network verifies the required property.

翻译：光强度变化等光学原因的图像,这些网络本身不易照明。 在本文件中,我们引入了一个形态神经网络,这种神经网络对照明变化具有如此强的特性。它基于近期的对数数学道德学(LMM)框架,即与逻辑图像处理模型(LIP)定义的数学生理学。这个模型有一个LIP添加法,用来模拟光度变化的图像。我们特别学会了与这些变化相适应的LIMM操作员的结构功能,即:LIP-additive Asplund距离图。图像结果显示,我们的神经网络验证了所需的属性。