Temperature‐Robust Learned Image Recovery for Shallow‐Designed Imaging Systems
Imaging systems are widely applied in harsh environments where the performance of shallow‐designed systems may deviate from expectation. As a representative scenario, environmental temperature variation may degrade image quality due to thermal defocus and sensor response, resulting in blur and noise. However, extensive athermalization in optics usually requires a complex design process and is limited by materials. Herein, a multibranch computational imaging scheme is developed, using emerging generative adversarial networks as the postprocessing to compensate for degradation of all kinds caused by thermal defocus and noise. In addition, a temperature controllable data acquisition, division, and mixture scheme is described to facilitate effective datasets for model robustness. Experiments on a vehicle lens and a mobile phone lens reveal that the proposed multibranch learned strategy notably increases image quality in the temperature range of 0–80 °C, and outperforms conventional athermalization in most instances, which is beneficial to lowering the design and manufacturing costs of imaging systems.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Temperature‐Robust Learned Image Recovery for Shallow‐Designed Imaging Systems ; day:23 ; month:09 ; year:2022 ; extent:9
Advanced intelligent systems ; (23.09.2022) (gesamt 9)
- Creator
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Chen, Wei
Qi, Bingyun
Liu, Xu
Li, Haifeng
Hao, Xiang
Peng, Yifan
- DOI
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10.1002/aisy.202200149
- URN
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urn:nbn:de:101:1-2022092415080580461979
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:30 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Chen, Wei
- Qi, Bingyun
- Liu, Xu
- Li, Haifeng
- Hao, Xiang
- Peng, Yifan
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