Before revision:  However,  due to the slight difference between simulated images, the low-accuracy of the Convolutional Neural Network ( CNN)and poor-diversity of the Generative Adversarial Network (GAN) make the reconstruction process low-accuracy, poor-efficiency, expensive-computation and high-manpower. In this study, an improved ReConNN model is proposed to address the mentioned weaknesses.
After revision:  However, due to the slight difference between simulated images, the low-accuracy of the Convolutional Neural Network ( CNN)and poor-diversity of the Generative Adversarial Network (GAN) to generate various reasonable images. Therefore, in this study, an improved ReConNN model is proposed to address the mentioned weaknesses.