Quang Trieu

Traditional analytical Zernike methods for tomographic encoding require multiplying large Zernike polynomial matrices, which leads to substantial computational overhead. Moreover, the inverse Zernike matrices must be precomputed and stored, resulting in significant memory consumption—an issue that becomes increasingly severe as the Zernike order or tomogram resolution grows.

We propose a fast, new, position-independent reconstruction method, to our knowledge, for coherence holography using deep learning (DL) method, dubbed fast deep coherence holography (FDCH). Our trained network was developed to reconstruct the complex object field at the location corresponding to the position of the camera, which opens the ability to apply angular spectrum propagation to reconstruct the 3D object.

We propose a reconstruction method for coherence holography using deep neural networks. cGAN and U-NET models were developed to reconstruct 3D complex objects from recorded interferograms. Our proposed methods, dubbed deep coherence holography (DCH), predict the non-diffracted fields or the sub-objects included in the 3D object from the captured interferograms, yielding better reconstructed objects than the traditional analytical imaging methods in terms of accuracy, resolution, and time.