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13 March 2024 Sparse optical neural architectures for quantitative phase imaging
Hasindu Kariyawasam, Kithmini Herath, Dushan Wadduwage, Chamira Edussooriya
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Proceedings Volume PC12852, Quantitative Phase Imaging X; PC128520X (2024) https://doi.org/10.1117/12.3008534
Event: SPIE BiOS, 2024, San Francisco, California, United States
Abstract
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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Hasindu Kariyawasam, Kithmini Herath, Dushan Wadduwage, and Chamira Edussooriya "Sparse optical neural architectures for quantitative phase imaging", Proc. SPIE PC12852, Quantitative Phase Imaging X, PC128520X (13 March 2024); https://doi.org/10.1117/12.3008534
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KEYWORDS
Phase imaging
Tunable filters
Microscopes
Microscopy
Neural networks
Optical design
Optical filters
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