  Deep Neural Networks (DNNs) are generated by sequentially performing linear
and non-linear processes. Using a combination of linear and non-linear
procedures is critical for generating a sufficiently deep feature space. The
majority of non-linear operators are derivations of activation functions or
pooling functions. Mathematical morphology is a branch of mathematics that
provides non-linear operators for a variety of image processing problems. We
investigate the utility of integrating these operations in an end-to-end deep
learning framework in this paper. DNNs are designed to acquire a realistic
representation for a particular job. Morphological operators give topological
descriptors that convey salient information about the shapes of objects
depicted in images. We propose a method based on meta-learning to incorporate
morphological operators into DNNs. The learned architecture demonstrates how
our novel morphological operations significantly increase DNN performance on
various tasks, including picture classification and edge detection.
