Fig. 1. Flow chart of slightly off-axis interferometric fringe demodulation based on VHQPI.

Fig. 2. Deep learning-assisted VHQPI. (a) Total network structure, combining uVID and HST with CNN respectively for phase reconstruction. (b) CNN1 takes a hologram as input and consists of three convolutional layers and a group of residual blocks to achieve compensation of background residuals by learning. (c) The CNN2 network structure is the same as CNN1, except that CNN2 combines the original hologram and the result of the first process into a two-channel input for advanced background compensation.

Fig. 3. The experiment results under the numerical simulation. (a) The FT method phase recovery result. (b) The phase recovery result of VHQPI. (c) The phase result reconstructed by DL-VHQPI. (d) The ground truth. (e–g) The difference between the phase results of the three methods (i.e. FT, VHQPI, DL-VHQPI) and the ground truth. (h) Quantitative error analysis of three methods. (i) The cross-section of the phase results of FT, VHQPI, DL-VHQPI, and ground truth, and (j1–j4) are the DIC views of the partially enlarged views of their corresponding phase maps respectively.

Fig. 4. Results of holographic experiments on HeLa cells. (a) Low-carrier-frequency high-contrast hologram collected by slightly off-axis interferometry system. (b) Corresponding spatial frequency spectrum. (c) The result of phase recovery by slightly off-axis holography using FT method under ×20 lens. (d) The result of phase recovery using DL-VHQPI. (e1–e4) and (f1–f4) correspond to the local amplification results of “Area1” and “Area2” for the two samples under different phase recovery methods. Where (e2, e4, f2, f4) are the corresponding DIC views, respectively. (g) and (h) The DIC views after partial magnification of the phase map in the corresponding red box. (i) The numerical distribution of the cross-section and detail-preservation feature of the DL-VHQPI.