斑点图案
散斑噪声
人工智能
相关性
光学
计算机科学
卷积神经网络
散射
鬼影成像
散斑成像
计算机视觉
物理
模式识别(心理学)
数学
几何学
作者
Ryosuke Mashiko,Jun Tanida,Makoto Naruse,Ryoichi Horisaki
出处
期刊:Applied Optics
[Optica Publishing Group]
日期:2023-10-10
卷期号:62 (31): 8327-8327
被引量:6
摘要
We present a method for speckle-correlation imaging with an extended field of view to observe spatially non-sparse objects. In speckle-correlation imaging, an object is recovered from a non-invasively captured image through a scattering medium by assuming shift-invariance of the optical process called the memory effect. The field of view of speckle-correlation imaging is limited by the size of the memory effect, and it can be extended by extrapolating the speckle correlation in the reconstruction process. However, spatially sparse objects are assumed in the inversion process because of its severe ill-posedness. To address this issue, we introduce a deep image prior, which regularizes the image statistics by using the structure of an untrained convolutional neural network, to speckle-correlation imaging. We experimentally demonstrated the proposed method and showed the possibility of extending the method to imaging through scattering media.
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