近年来,深度学习被广泛应用于计算成像中,并取得了令人瞩目的成果,已成为该领域的研究热点。为了深入了解现有基于深度学习的方法是如何解决众多计算成像问题的,主要介绍了该方法的基本理论和实施步骤,然后以散射成像、数字全息及计算鬼成像中的应用为例具体介绍该方法的有效性和优越性。汇总对比了一些典型应用,并对基于深度学习的计算成像方法进行了总结和展望。

In recent years, deep learning (DL) has been widely used in computational imaging (CI) and has achieved remarkable results; as such, DL has become a research hotspot in this field. To gain an in-depth understanding of how DL-based CI works, this manuscript mainly introduces the basic theory and implementation steps of DL as well as its applications in scattering imaging, digital holography, and computational ghost imaging to demonstrate its effectiveness and superiority. Some typical applications of DL in CI are summarized and compared herein, and the CI methods based on deep learning are prospected.