中国物理B ›› 2023, Vol. 32 ›› Issue (10): 104203-104203.doi: 10.1088/1674-1056/acd8b2
Defogging computational ghost imaging via eliminating photon number fluctuation and a cycle generative adversarial network
Yuge Li(李玉格) and Deyang Duan(段德洋)†
- School of Physics and Physical Engineering, Qufu Normal University, Qufu 273165, China
Defogging computational ghost imaging via eliminating photon number fluctuation and a cycle generative adversarial network
Yuge Li(李玉格) and Deyang Duan(段德洋)†
- School of Physics and Physical Engineering, Qufu Normal University, Qufu 273165, China
摘要： Imaging through fluctuating scattering media such as fog is of challenge since it seriously degrades the image quality. We investigate how the image quality of computational ghost imaging is reduced by fluctuating fog and how to obtain a high-quality defogging ghost image. We show theoretically and experimentally that the photon number fluctuations introduced by fluctuating fog is the reason for ghost image degradation. An algorithm is proposed to process the signals collected by the computational ghost imaging device to eliminate photon number fluctuations of different measurement events. Thus, a high-quality defogging ghost image is reconstructed even though fog is evenly distributed on the optical path. A nearly 100% defogging ghost image is obtained by further using a cycle generative adversarial network to process the reconstructed defogging image.
中图分类号: (Image forming and processing)
- 42.30.Va