0.8% Nyquist computational ghost imaging via non-experimental deep learning
| العنوان: | 0.8% Nyquist computational ghost imaging via non-experimental deep learning |
|---|---|
| المؤلفون: | Haotian Song, Xiaoyu Nie, Hairong Su, Hui Chen, Yu Zhou, Xingchen Zhao, Tao Peng, Marlan O. Scully |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Image and Video Processing (eess.IV), FOS: Electrical engineering, electronic engineering, information engineering, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Electrical and Electronic Engineering, Physical and Theoretical Chemistry, Electrical Engineering and Systems Science - Image and Video Processing, Atomic and Molecular Physics, and Optics, Electronic, Optical and Magnetic Materials |
| الوصف: | We present a framework for computational ghost imaging based on deep learning and customized pink noise speckle patterns. The deep neural network in this work, which can learn the sensing model and enhance image reconstruction quality, is trained merely by simulation. To demonstrate the sub-Nyquist level in our work, the conventional computational ghost imaging results, reconstructed imaging results using white noise and pink noise via deep learning are compared under multiple sampling rates at different noise conditions. We show that the proposed scheme can provide high-quality images with a sampling rate of 0.8% even when the object is outside the training dataset, and it is robust to noisy environments. This method is excellent for various applications, particularly those that require a low sampling rate, fast reconstruction efficiency, or experience strong noise interference. 10 pages, 6 figures |
| اللغة: | English |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::83857137d7571e164f10abcf93b77a3c http://arxiv.org/abs/2108.07673 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....83857137d7571e164f10abcf93b77a3c |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |