Resumen
Slightly off-axis digital holographic microscopy (DHM) is the extension of digital holography imaging technology toward high-throughput modern optical imaging technology. However, it is difficult for the method based on the conventional linear Fourier domain filtering to solve the imaging artifacts caused by the spectral aliasing problem. In this article, we propose a novel high-accuracy, artifacts-free, single-frame, digital holographic phase demodulation scheme for low-carrier-frequency holograms, which incorporates the physical model into a conventional deep neural network (DNN) without training beforehand based on a massive dataset. Although the conventional end-to-end deep learning (DL) method can achieve high-accuracy phase recovery directly from a single-frame hologram, the massive datasets and ground truth collection can be prohibitively laborious and time-consuming. Our method recognizes such a low-carrier frequency fringe demodulation process as a nonlinear optimization problem, which can reconstruct the artifact-free phase details gradually from a single-frame hologram. The phase resolution target and simulation experiment results quantitatively demonstrate that the proposed method possesses better artifact suppression and high-resolution imaging capabilities than the physical methods. In addition, the live-cell experiment also indicates the practicality of the technique in biological research.