Meta-surface lens and deep learning process toward practical LWIR imaging devices

Wontaek Seo; Duhyun Lee; Yongseop Yoon; Soonkeun Chang; Jang-Woo You; Byung-Kyu Lee; Seyoon Kim

doi:10.1117/12.2674111

10 October 2023 Meta-surface lens and deep learning process toward practical LWIR imaging devices

Wontaek Seo, Duhyun Lee, Yongseop Yoon, Soonkeun Chang, Jang-Woo You, Byung-Kyu Lee, Seyoon Kim

Proceedings Volume 12687, Infrared Sensors, Devices, and Applications XIII; 1268709 (2023) https://doi.org/10.1117/12.2674111
Event: SPIE Optical Engineering + Applications, 2023, San Diego, California, United States

Abstract

We report the useful approach toward the practical imaging solution for long-wavelength infrared (LWIR). In order to make use of the LWIR sensors conveniently in everyday life, the imaging module needs to be slim and small so that it can be mounted inside end-user devices without big difficulties. At the same time, the image qualities should have a sufficient level to guarantee that people can easily identify object shapes and recognize temperature differences when they see the resultant images. In this paper, we focus on those two crucial points for the practical LWIR imaging device. First, to make the compact optical system, we adopted a thin meta-surface lens of a focal length 2 mm which is showing the effective total top length (TTL) less than 3 mm. Second, to enhance the image sharpness degraded relatively due to the lens, the deep learning method of the U-net model is introduced. The patterns of the USAF resolution target chart indicate the increase of modulation value by 3~8 times after applying the learning process. We believe that our work helps to expand the pragmatic application area of the LWIR imaging sensors in the near future.

Conference Presentation

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wontaek Seo, Duhyun Lee, Yongseop Yoon, Soonkeun Chang, Jang-Woo You, Byung-Kyu Lee, and Seyoon Kim "Meta-surface lens and deep learning process toward practical LWIR imaging devices", Proc. SPIE 12687, Infrared Sensors, Devices, and Applications XIII, 1268709 (10 October 2023); https://doi.org/10.1117/12.2674111

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