On the interplay between physical and content priors in deep learning for computational imaging
© 2020 Optical Society of America. Deep learning (DL) has been applied extensively in many computational imaging problems, often leading to superior performance over traditional iterative approaches. However, two important questions remain largely unanswered: First, how well can the trained neural n...
Main Authors: | , , , , , |
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Other Authors: | |
Format: | Article |
Language: | English |
Published: |
Optical Society of America
2022
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Online Access: | https://hdl.handle.net/1721.1/138458.2 |