In this paper,we present an edge detection scheme based on ghost imaging(GI)with a holistically-nested neural network.The so-called holistically-nested edge detection(HED)network is adopted to combine the fully convol...In this paper,we present an edge detection scheme based on ghost imaging(GI)with a holistically-nested neural network.The so-called holistically-nested edge detection(HED)network is adopted to combine the fully convolutional neural network(CNN)with deep supervision to learn image edges efectively.Simulated data are used to train the HED network,and the unknown object’s edge information is reconstructed from the experimental data.The experiment results show that,when the compression ratio(CR)is 12.5%,this scheme can obtain a high-quality edge information with a sub-Nyquist sampling ratio and has a better performance than those using speckle-shifting GI(SSGI),compressed ghost edge imaging(CGEI)and subpixel-shifted GI(SPSGI).Indeed,the proposed scheme can have a good signal-to-noise ratio performance even if the sub-Nyquist sampling ratio is greater than 5.45%.Since the HED network is trained by numerical simulations before the experiment,this proposed method provides a promising way for achieving edge detection with small measurement times and low time cost.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.61871234 and 62001249).
文摘In this paper,we present an edge detection scheme based on ghost imaging(GI)with a holistically-nested neural network.The so-called holistically-nested edge detection(HED)network is adopted to combine the fully convolutional neural network(CNN)with deep supervision to learn image edges efectively.Simulated data are used to train the HED network,and the unknown object’s edge information is reconstructed from the experimental data.The experiment results show that,when the compression ratio(CR)is 12.5%,this scheme can obtain a high-quality edge information with a sub-Nyquist sampling ratio and has a better performance than those using speckle-shifting GI(SSGI),compressed ghost edge imaging(CGEI)and subpixel-shifted GI(SPSGI).Indeed,the proposed scheme can have a good signal-to-noise ratio performance even if the sub-Nyquist sampling ratio is greater than 5.45%.Since the HED network is trained by numerical simulations before the experiment,this proposed method provides a promising way for achieving edge detection with small measurement times and low time cost.
