Abstract

In recent years, significant advancements have been made in the field of computational imaging, particularly due to the application of deep learning methods to imaging problems. However, only a few studies related to deep learning have examined the impact of diffraction distance on image restoration. In this paper, the effect of diffraction distance on image restoration is investigated based on the PhysenNet neural network. A theoretical framework for diffraction images at various diffraction distances is provided along with the applicable propagators. In the experiment, the PhysenNet network is selected to train on diffraction images with different distances and the impact of using different propagators on network performance is studied. Optimal propagators required to recover images at different diffraction distances are determined. Insights obtained through these experiments can expand the scope of neural networks in computational imaging.

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