Open AccessPhoton-limited imaging through scattering medium based on deep learning
Author(s) -
Lei Sun,
Jianhong Shi,
Xiaoyan Wu,
Yiwei Sun,
Guihua Zeng
Publication year - 2019
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.27.033120
Subject(s) - speckle pattern , optics , scattering , light scattering , computer science , physics , speckle noise , photon , ghost imaging , artificial intelligence , signal (programming language) , pixel , computer vision , programming language
Imaging under ultra-weak light conditions is affected by Poisson noise heavily. The problem becomes worse if a scattering media is present in the optical path. Speckle patterns detected under ultra-weak light condition carry very little information which makes it difficult to reconstruct the image. Off-the-shelf methods are no longer available in this condition. In this paper, we experimentally demonstrate the use of a deep learning network to reconstruct images through scattering media under ultra-weak light illumination. The weak light limitation of this method is analyzed. The random Poisson detection under weak light condition obtains partial information of the object. Based on this property, we demonstrated better performance of our method by enlarging the training dataset with multiple detections of the speckle patterns. Our results demonstrate that our approach can reconstruct images through scattering media from close to 1 detected signal photon per pixel (PPP) per image.