Fig. 2: Performance of trained 3D CNN model in testing data.
From: Three-dimensional coherent X-ray diffraction imaging via deep convolutional neural networks
a 3D isointensities of test input coherent X-ray diffraction patterns, which were not used for training. Here, the colors correspond to the radial distance from the origin of the reciprocal space. b Isointensity of the ground truth for the corresponding particles. c The complex-valued image predicted by the CNN model. Here, the isosurface plots in (c) and (b) are obtained by the amplitude of the particles and the corresponding color represents the phase distribution on their surfaces.
