动态缩放
缩放比例
散射
动态光散射
计算机科学
深度学习
人工智能
物理
统计物理学
生物系统
材料科学
光学
纳米技术
数学
几何学
纳米颗粒
生物
摘要
Ghost imaging (GI) through dynamic and complex scattering media remains challenging. The existence of dynamic scattering gives rise to a failure of GI schemes. Here, we report a deep learning-enhanced GI scheme with supervised corrections (SCGI) of dynamic scaling factors to realize high-resolution ghost reconstruction through dynamic and complex scattering media. The SCGI scheme is developed to approximate the variation of dynamic scaling factors in an optical channel and correct the recorded light intensities with a Gaussian prior. An untrained neural network powered by regularization by denoising for the SCGI scheme (SCGI-URED) is developed to further recover high-visibility ghost images. Experimental results demonstrate that high-resolution and high-visibility GI can be realized in dynamic and complex scattering media. The proposed method provides a reliable tool for implementing high-resolution and high-visibility GI through dynamic and complex scattering media and could give an impetus to developing dynamic scattering imaging in real-world scenarios.
科研通智能强力驱动
Strongly Powered by AbleSci AI