Robust Holographic Reconstruction by Deep Learning with One Frame

Abstract

A robust method is proposed to reconstruct images with only one hologram in digital holography by introducing a deep learning (DL) network. The U-net neural network is designed according to DL principles and trained by the image data set collected using phase-shifting digital holography (PSDH). The training data set was established by collecting thousands of reconstructed images using PSDH. The proposed method can complete the holography reconstruction with only a single hologram and then benefits the space bandwidth product and relaxes the storage loads of PSDH. Compared with the results of PSDH, the results of deep learning are immune to most disturbances, including reference tilt, phase-shift errors, and speckle noise. Assisted by a GPU processor, the proposed reconstruction method can reduce the consumption time to almost one percent of the time needed by two-step PSDH. This method is expected to be capable of holography imaging with a single hologram, with high capacity, efficiently in the digital holography applications.