Mathematical morphology provides powerful nonlinear operators for a variety of image processing tasks such as filtering, segmentation, and edge detection. In this paper, we propose a way to use these nonlinear operators in an end-toend deep learning framework and illustrate them on different applications. We demonstrate on various examples that new layers making use of the morphological non-linearities are complementary to convolution layers. These new layers can be used to integrate the non-linear operations and pooling into a joint operation. We finally enhance results obtained in boundary detection using this new family of layers with just 0.01% of the parameters of competing state-of-the-art methods.

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