研究目的
To solve the nonlinear electromagnetic inverse scattering problem using a complex-valued convolutional neural network (complex-CNN) for high-quality image reconstruction in real-time.
研究成果
The complex-CNN model effectively solves electromagnetic inverse scattering problems, providing high-quality reconstructions on human model data. It demonstrates great potential for large-scale inverse scattering problems, balancing accuracy and computational cost.
研究不足
The study does not explicitly mention limitations, but potential areas for optimization could include further reducing computational costs and enhancing the model's adaptability to various scattering scenarios.
1：Experimental Design and Method Selection:
The study employs a complex-CNN model to address the electromagnetic inverse scattering problem, focusing on human models with different postures. The input data for the complex-CNN are reconstructed images from scattering data using the Back-propagation algorithm.
2：Sample Selection and Data Sources:
A dataset of 1000 human body images with different postures is used, with 750 for training and 250 for testing.
3：List of Experimental Equipment and Materials:
Not explicitly mentioned.
4：Experimental Procedures and Operational Workflow:
The scenario involves a monochromatic multistatic/multiview configuration for generating scattering data. The complex-CNN processes the data by dividing it into real and imaginary parts, similar to RGB-color images.
5：Data Analysis Methods:
The study analyzes model parameters such as the number of kernels, kernel size, and patch size to assess their impact on model accuracy and computational cost.
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