Image Reconstruction With Deep CNN for Mirrored Aperture Synthesis

In mirrored aperture synthesis (MAS), the existing brightness temperature image reconstruction methods include inverse cosine transform and impulse matrix reconstruction methods. However, the quality of the MAS brightness temperature images reconstructed by the existing methods is still poor and needs to be improved. This article proposes a method of MAS brightness temperature image reconstruction with deep convolutional neural network (CNN). The network includes two fully connected (FC) layers, multiple convolutional layers, and deconvolutional layers, which realize the image reconstruction for MAS. This method uses deep CNN to learn the MAS image reconstruction mapping and system errors, so as to improve the performance of the brightness temperature image reconstruction. Both simulation and experimental results verify that the performance of the proposed MAS-CNN method is better than the existing MAS image reconstruction methods.

Automated summary

This article proposes a method of MAS brightness temperature image reconstruction with deep convolutional neural network (CNN), which improves the reconstruction performance by learning the image mapping and system errors. Simulation and experimental results demonstrate that the proposed MAS-CNN method outperforms existing MAS image reconstruction methods.