| Citation: | LI H，ZHONG H P，ZHANG P，et al. Multi-shift interferometric phase filtering method based on convolutional neural network[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（6）：2043-2050 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0805
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To improve the performance of phase filtering in interferometric signal processing, a multi-shift interferometric phase filtering method based on convolutional neural networks is proposed. First, the phase shift principle is explained using the interferometric phase noise model. Then multiple convolutional neural network denoisers are built based on the phase shift principle and used to filter the interferometric phases with different shifts. Subsequently, a number of denoisers for convolutional neural networks are constructed using the notion of phase shift and employed to filter the interferometric phases with various shifts. Finally, multiple denoised phases are generated. The neural network is then used to calculate the pixel weights and fuse the multiple denoising results, resulting in a higher-quality result. The plenty denoising outputs are then fused and the pixel weights are calculated using the neural network to provide a higher-quality output. The simulated and real data experiments show that the proposed method retains more detail and has a lower root-mean-square error and the number of residues than the traditional methods. Experiments using both simulated and real data demonstrate that the suggested approach outperforms the conventional methods in terms of detail retention, root-mean-square error, and residue count.
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