Learning to Sense for Coded Diffraction Imaging
In this paper, we present a framework to learn illumination patterns to improve the quality of signal recovery for coded diffraction imaging. We use an alternating minimization-based phase retrieval method with a fixed number of iterations as the iterative method. We represent the iterative phase re...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-12-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/24/9964 |
| Summary: | In this paper, we present a framework to learn illumination patterns to improve the quality of signal recovery for coded diffraction imaging. We use an alternating minimization-based phase retrieval method with a fixed number of iterations as the iterative method. We represent the iterative phase retrieval method as an unrolled network with a fixed number of layers where each layer of the network corresponds to a single step of iteration, and we minimize the recovery error by optimizing over the illumination patterns. Since the number of iterations/layers is fixed, the recovery has a fixed computational cost. Extensive experimental results on a variety of datasets demonstrate that our proposed method significantly improves the quality of image reconstruction at a fixed computational cost with illumination patterns learned only using a small number of training images. |
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| ISSN: | 1424-8220 |