基于多尺度残差生成对抗网络的微观结构数据重构OA

Microstructural data,which possesses complex internal structures,is a type of material data that is significant for the application fields of microspace data,such as geological exploration,materials science,and biomedicine.For many years,nu-merical simulation and statistical analysis have been widely applied in the research of microspace data reconstruction.However,with the increasing complexity of data,these traditional methods have shown limitations in meeting the high precision require-ments for data reconstruction and have imposed a significant load on CPU resources.In recent years,the technology of deep learning has seen rapid development,and Generative Adversarial Networks(GAN)have become an important research area for microstructural data reconstruction due to their excellent ability to handle nonlinearity,multi-scale and complexity.This paper proposes a microstructural data image reconstruction algorithm based on Multi-Scale Residual Generative Adversarial Networks(MSR-GAN),which integrates attention mechanisms and residual connections.The model adopts a progressive growth multi-scale feature extraction strategy to generate images from low resolution to high resolution,in order to capture both global and lo-cal details.The experimental results show that,compared to traditional numerical simulation and other GAN methods,MSR-GAN exhibits superior performance in the field of microstructural data reconstruction,thereby verifying the effectiveness and practicality of the algorithm proposed in this paper.