  In High Energy Physics experiments Particle Flow (PFlow) algorithms are
designed to provide an optimal reconstruction of the nature and kinematic
properties of the particles produced within the detector acceptance during
collisions. At the heart of PFlow algorithms is the ability to distinguish the
calorimeter energy deposits of neutral particles from those of charged
particles, using the complementary measurements of charged particle tracking
devices, to provide a superior measurement of the particle content and
kinematics. In this paper, a computer vision approach to this fundamental
aspect of PFlow algorithms, based on calorimeter images, is proposed. A
comparative study of the state of the art deep learning techniques is
performed. A significantly improved reconstruction of the neutral particle
calorimeter energy deposits is obtained in a context of large overlaps with the
deposits from charged particles. Calorimeter images with augmented finer
granularity are also obtained using super-resolution techniques.
