Deep morphological networks

Highlights：

• A novel morphological deep learning framework with learned mathematical morphology operators.

• We are the first to attempt to learn the weights of non-approximated mathematical morphology operators end-to-end in deep learning frameworks.

• A replacement for the standard max pooling in convolutional neural networks with a learned morphological pooling that proves to be experimentally beneficial.

• We propose a mixed morphological and convolutional neural network that performs edge detection with results competitive with state-of-the-art. In addition, this network is trained from scratch, on the contrary to state-of-the-art that make use of pretrained weights.

• We propose a fully morphological neural network for image denoising that present better performance than similar fully convolutional neural network for this task.