典型文献
Deep learning-based scattering removal of light field imaging
文献摘要：
Light field imaging has shown significance in research fields for its high-temporal-resolution 3D imaging ability.However,in scenes of light field imaging through scattering,such as biological imaging in vivo and imaging in fog,the quality of 3D reconstruction will be severely reduced due to the scattering of the light field information.In this paper,we propose a deep learning-based method of scattering removal of light field imaging.In this method,a neural network,trained by simulation samples that are generated by light field imaging forward models with and without scattering,is utilized to remove the effect of scattering on light fields captured experimentally.With the deblurred light field and the scattering-free forward model,3D reconstruction with high resolution and high contrast can be realized.We demonstrate the proposed method by using it to realize high-quality 3D reconstruction through a single scattering layer experimentally.
文献关键词：
中图分类号：
作者姓名：
Weihao Wang;Xing Zhao;Zhixiang Jiang;Ya Wen
作者机构：
Institute of Modern Optics,Nankai University,Tianjin 300350,China;Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology,Tianjin 300350,China
文献出处：
引用格式：
[1]Weihao Wang;Xing Zhao;Zhixiang Jiang;Ya Wen-.Deep learning-based scattering removal of light field imaging)[J].中国光学快报（英文版）,2022(04):16-22
A类：
deblurred
B类：
Deep,learning,scattering,removal,light,imaging,Light,has,shown,significance,research,fields,its,high,temporal,resolution,ability,However,scenes,through,such,biological,vivo,fog,quality,reconstruction,will,be,severely,reduced,due,information,In,this,paper,deep,method,neural,network,trained,by,simulation,samples,that,are,generated,forward,models,without,utilized,remove,effect,captured,experimentally,With,free,contrast,realized,We,demonstrate,proposed,using,single,layer
AB值：
0.474066
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机标中图分类号，由域田数据科技根据网络公开资料自动分析生成，仅供学习研究参考。
