CNN-based morphological decomposition of X-ray images for details and defects contrast enhancement

This paper introduces a new learning based framework for X-ray images that relies on a morphological decomposition of the signal into two main components, separating images into local textures and piecewise smooth (cartoon) parts. The piecewise smooth component corresponds to the spatial variation of the average density of the objects, whereas the local texture component presents the inspected objects singularities. Our method builds on two convolutional neural network (CNN) branches to decompose an input image into its two morphological components. This CNN is trained with synthetic data, generated by randomly picking piecewise smooth and singular patterns in a parametric dictionary and enforcing the sum of the CNN branches to approximate the identity mapping. We demonstrate the relevance of the decomposition by enhancing the local textures component compared to the piecewise smooth one. Those enhanced images compare favorably to the ones obtained with existing works destined to visualize High Dynamic Range (HDR) images such as tone-mapping algorithms.