Mr. Masato Gocho Profile

Masato Gocho

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Proceedings Article | 20 November 2024 Poster + Paper

Deep learning-enhanced fusion of low coherence optical interferometry and ghost imaging

Alifu Xiafukaiti, Huyan Decai, Masato Gocho, Ryuhei Takahashi, Tatsuo Shiina

Proceedings Volume 13196, 131960Y (2024) https://doi.org/10.1117/12.3031579

KEYWORDS: Image restoration, Structured light, Deep learning, Scattering media, Image quality, Time metrology, Education and training, Target detection, Optical interferometry, Neural networks

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Non-contact and non-invasive target detection in scattering media is a crucial task for advancements in biomedical imaging and environmental measurements. A novel sensing approach which combination of Michelson interferometer and ghost imaging (MIGI) is designed and developed for reconstructing the real image of target sample in scattering media. The limitations of optical interferometry, such as the need of scanning time for two-dimension measurements, and the inability of ghost imaging to capture cross-sectional images, can be effectively mitigated through their combination. However, this technique necessitates numerous illuminations of random structured light in ghost imaging to reconstruct the image of the target with high quality, thereby extending the measurement time. To address this issue and facilitate real-time measurement in MIGI, we reconstructed the real image close to the target sample being measured by passing degraded images obtained through short-term measurement through deep learning. This approach significantly reduces the number of measurements required to obtain a clear image in the simulation by 90%. In practical experiments, the number of measurements needed to achieve an equivalent structural similarity index method (SSIM) value is reduced. This paper discusses analysis of measurement time and SSIM based on the values of the dataset used for model construction.

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