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Many nuclear safety applications need fast, portable, and accurate imagers tobetter locate radiation sources. The Rotating Scatter Mask (RSM) system is anemerging device with the potential to meet these needs. The main challenge isthe under-determined nature of the data acquisition process: the dimension ofthe measured signal is far less than the dimension of the image to bereconstructed. To address this challenge, this work aims to fuse model-basedsparsity-promoting regularization and a data-driven deep neural networkdenoising image prior to perform image reconstruction. An efficient algorithmis developed and produces superior reconstructions relative to currentapproaches.

Fusing Sparsity with Deep Learning for Rotating Scatter Mask Gamma Imaging