Absolute-value max pooling in 2D convolutional neural networks
If you need to implement such an absolute value max pooling you can convert the output of the convolutional layer to an absolute value and then apply a maxpooling.
Moreover such thought is not necessary as maxpooling works with the output of the convolutional layers and is not influenced by the images directly. By which I mean that the convolutonal layers extract the most important features from the image be it positive or negative that plays a role in predicting a correct output
So for example if a low intensity pixel affects the identification more then it will be given a positive large weight compared to a pixel value that is less influential.