HIGH PERFORMANCE COMPUTATIONAL INTELLIGENCE FOR COHERENT DIFFRACTION DATA ANALYSIS AND IMAGING

Coherent Diffraction Imaging (CDI) is a lens-less technique that allows imaging of matter at a spatial resolution not limited by lens aberrations. This technique exploits the measured diffraction pattern of a coherent beam scattered by periodic and non–periodic objects to retrieve spatial information. The diffracted intensity, for weak–scattering objects, is proportional to the modulus of the Fourier Transform of the object density distribution. Any phase information, needed to retrieve the sample density, has to be retrieved by means of suitable algorithms. This work presents a new approach based on Computational Intelligence, in particular on Genetic Algorithms, to face the phase retrieval problem. This new approach, called Memetic Phase Retrieval, is described, along with its implementation specifically designed and optimized for High Performance Computing hardware. Tests on both simulated and experimental data are performed, showing a remarkable performance improvement with respect to standard algorithms. Memetic Phase Retrieval proves to be a new powerful tool for the study of matter via CDI. Moreover, it represents a novelty, laying the foundations for a more extensive use of Computational Intelligence in the field of CDI and opening new perspectives in those applications in which reliable phase retrieval is necessary.