Out-of-focus artifact removal for Fresnel incoherent correlation holography by deep learning

Abstract

Fresnel incoherent correlation holography (FINCH) is a promising three-dimensional (3D) imaging method due to its incoherent illumination and inline optical configuration. However, due to information crosstalk, especially out-of-focus artifacts, the axial resolution of FINCH 3D imaging is limited, and the relationship between the artifacts and the reconstructed image is complex and nonlinear, which is difficult to obtain by traditional optical measurement methods. Here, inspired by the superiority of deep learning in nonlinear mapping over traditional physical methods, we propose and demonstrate a deep learning based out-of-focus artifact removal method for FINCH reconstruction, in which a out-of-focus segmentation convolutional neural network (CNN) is designed for obtaining accurate nonlinear relationship between the artifacts and the reconstructed image by training dataset and optimizing parameters, thereby eliminating information crosstalk in the reconstructed image. Both optical experiment and performance analysis demonstrate the effectiveness and superiority of the designed CNN in realizing out-of-focus artifacts removal. Importantly, this deep learning-based out-of-focus artifacts removal method will provide a useful strategy for realizing high quality 3D imaging of FINCH.