Is it possible to formally define one of the important capabilities of human mind, the intuition, in a mathematical sense? Can we use this definition to develop more robust CNN (Convolutional Neural Networks) model? Through this method, can we develop an algorithm that can recognize incomplete images? In this study, we attempt to find partial answers to the above questions. First, we examined how the performance of CNN algorithms decreased by reducing the amount of information in the test set images in a controlled manner. In order to reduce this decrease, we made a mathematical definition of intuition that could enrich the convolutional neural networks. We used this definition and the intuition module we created to improve the filter outputs in the convolution layer. We used MNIST number dataset to measure the performance of this new CNN model, enriched with an intuition module, called, DidEye performance. Experimental results show that the suggested DidEye model is much more robust and provides higher performance compared to the classical CNN model in a test set containing incomplete images.