宽带电磁图像卷积神经网络盲恢复方法研究

在利用抛物反射面对电磁干扰源成像过程中，由于系统衍射受限及成像频带较宽，导致干扰源成像模糊，分辨率低，难以分辨，不同频率不同区域干扰源所成图像分辨率不同，采用已有超分辨算法难以提高分辨率。为了实现宽带电磁图像的盲复原, 应用卷积神经网络的方法。网络训练是直接输入模糊图像，不假设任何特定的模糊和噪声模型情况下，重建出高质量图像。实验和仿真结果证明了卷积神经网络盲恢复方法在宽频带不同成像区域下表现了优于其他盲恢复算法的优势。

Research on blind recovery method of wideband electromagnetic image convolutional neural network

In the process of electromagnetic interference sources imaging testing using parabolic reflection, the diffraction phenomenon of the system leads to blurred and low resolution images. Interference sources have different resolution capabilities in different areas and frequencies, so it's difficult to enhance image resolution by using existed super-resolution algorithm. In order to realize the blind recovery of wideband electromagnetic images, a method based on convolutional neural network is proposed. Network training is the process which directly inputs a blurred image and reconstructs a high quality image without assuming any particular blur and noise model. Both experiment and simulation results demonstrate that the convolutional neural network blind recovery method outperforms other blind recovery calculations in different imaging regions of wide frequency band.