Complex-domain-enhancing neural network for large-scale coherent imaging

Large-scale computational imaging can provide remarkable space-bandwidth product that is beyond the limit of optical systems.In coherent imaging(CI),the joint reconstruction of amplitude and phase further expands the information throughput and sheds light on label-free observation of biological samples at micro-or even nano-levels.The existing large-scale CI techniques usually require scanning/modulation multiple times to guarantee measurement diversity and long exposure time to achieve a high signal-to-noise ratio.Such cumbersome procedures restrict clinical applications for rapid and lowphototoxicity cell imaging.In this work,a complex-domain-enhancing neural network for large-scale CI termed CI-CDNet is proposed for various large-scale CI modalities with satisfactory reconstruction quality and efficiency.CI-CDNet is able to exploit the latent coupling information between amplitude and phase(such as their same features),realizing multidimensional representations of the complex wavefront.The cross-field characterization framework empowers strong generalization and robustness for various coherent modalities,allowing high-quality and efficient imaging under extremely low exposure time and few data volume.We apply CI-CDNet in various large-scale CI modalities including Kramers–Kronigrelations holography,Fourier ptychographic microscopy,and lensless coded ptychography.A series of simulations and experiments validate that CI-CDNet can reduce exposure time and data volume by more than 1 order of magnitude.We further demonstrate that the high-quality reconstruction of CI-CDNet benefits the subsequent high-level semantic analysis.