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In medical imaging, precise and reliable images are very important. However, the quality of medical images is sometimes limited by low-event statistics owing to the low sensitivity of the detectors commonly used in radiology. On the other hand, long exposure to radiation and long inspection duration can become a burden for patients. In this paper, we propose a method for generating high-quality images of gamma ray sources from low statistic data by using machine learning methods based on dictionary learning and sparse coding. As the first application, we generated a high-quality image of ¹³⁷Cs, which emits 662-keV gamma rays, from low-event statistics measured using a Compton camera. We simulated with Geant4 various geometries of the gamma-ray source (¹³⁷Cs; 662 keV) as measured with a Compton camera by Geant4. Then, complete sets of low-resolution and high-resolution dictionaries were …