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Recently, various optical neural architectures have been designed to perform imaging and all-optical image processing. These designs consist of several optical masks with optimized transmission coefficients for the task. Designing sparse optical masks for them is important as it can promote different aspects of the design such as ease of fabrication and power efficiency of the design. To this end, inspired by the sparse filter designs, we propose training optical neural architectures with a regularization promoting sparsity in the masks. As preliminary results, we demonstrate a D2NN design for QPI achieving a sparsity of 33% with a performance degradation of only 12%.
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